mind really does matter: the neurobiology of placebo - diva

ACTAUNIVERSITATIS

UPSALIENSISUPPSALA

2012

Digital Comprehensive Summaries of Uppsala Dissertationsfrom the Faculty of Social Sciences 82

Mind really does matter

The Neurobiology of Placebo-induced AnxietyRelief in Social Anxiety Disorder

VANDA FARIA

ISSN 1652-9030ISBN 978-91-554-8478-1urn:nbn:se:uu:diva-181548

Dissertation presented at Uppsala University to be publicly examined in Auditorium Minus,Gustavianum, Akademigatan 3, Uppsala, Friday, November 9, 2012 at 13:15 for the degree ofDoctor of Philosophy. The examination will be conducted in English.

AbstractFaria, V. 2012. Mind really does matter: The Neurobiology of Placebo-induced AnxietyRelief in Social Anxiety Disorder. Acta Universitatis Upsaliensis. Digital ComprehensiveSummaries of Uppsala Dissertations from the Faculty of Social Sciences 82. 92 pp. Uppsala.ISBN 978-91-554-8478-1.

The placebo effect, a beneficial effect attributable to a treatment containing no specificproperties for the condition being treated, has been demonstrated in a variety of medicalconditions. This thesis includes four studies aimed at increasing our knowledge on theneurobiology of placebo. Study I, a review of the placebo neuroimaging literature, suggestedthat the anterior cingulate cortex (ACC) may be a common site of action for placebo responses.However, because placebo neuroimaging studies in clinical disorders are largely lacking, theclinical relevance of this needs further clarification. The subsequent three empirical studieswere thus designed from a clinical perspective. Using positron emission tomography (PET)these studies investigated the underlying neurobiology of sustained placebo responses inpatients with social anxiety disorder (SAD), a disabling psychiatric condition that nonethelessmay be mitigated by placebo interventions. Study II demonstrated that serotonergic genepolymorphisms affect anxiety-induced neural activity and the resultant placebo phenotype. Inparticular, anxiety reduction resulting from placebo treatment was tied to the attenuating effectsof the TPH2 G-703T polymorphism on amygdala activity. Study III further compared the neuralresponse profile of placebo with selective serotonin reuptake inhibitors (SSRIs), i.e the first-linepharmacological treatment for SAD. A similar anxiety reduction was noted in responders of bothtreatments. PET-data further revealed that placebo and SSRI responders had similar decreasesof the neural response in amygdala subregions including the left basomedial/basolateral (BM/BLA) and the right ventrolateral (VLA) sections. To clarify whether successful placebo andSSRI treatments operate via similar or distinct neuromodulatory pathways, study IV focusedon the connectivity patterns between the amygdala and prefrontal cortex that may be crucialfor normal emotion regulation. In responders of both treatment modalities, the left amygdala(BM/BLA) exhibited negative coupling with the dorsolateral prefrontal cortex and the rostralACC as well as a shared positive coupling with the dorsal ACC. This may represent sharedtreatment mechanisms involving improved emotion regulation and decreased rumination. Thisthesis constitutes a first step towards better understanding of the neurobiology of placebo inthe treatment of anxiety, including the neural mechanisms that unite and segregate placebo andSSRI treatment.

Keywords: Placebo effect, anxiolysis, SAD, PET, TPH2 G-703T polymorphism, SSRIs,amygdala subregions, prefrontal cortex.

Vanda Faria, Uppsala University, Department of Psychology, Box 1225, SE-751 42 Uppsala,Sweden.

© Vanda Faria 2012

ISSN 1652-9030ISBN 978-91-554-8478-1urn:nbn:se:uu:diva-181548 (http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-181548)

“Be not afraid of life. Believe that life is worth living, and your belief willhelp create the fact.”

William James

To my family ♥

List of Papers

This thesis is based on the following papers, which are referred to in the text by their Roman numerals.

I Faria, V., Fredrikson, M., Furmark, T. (2008). Imaging the pla-

cebo response: A neurofunctional review. European Neuropsy-chopharmacology, 18(7):473-485.

II Furmark, T., Appel, L., Henningsson, S., Åhs, F., Faria, V., Linnman, C., Pissiota, A., Frans, Ö., Bani, M., Bettica, P., Pich, EM., Jacobsson, E., Wahlstedt, K., Oreland, L., Långström, B., Eriksson, E., Fredrikson, M. (2008). A link between serotonin-related gene polymorphisms, amygdala activity, and placebo induced relief from social anxiety. Journal of Neuroscience, 28(49):13066-13074.

III Faria, V., Appel, L., Åhs, F., Linnman, C., Pissiota, A., Frans, Ö., Bani, M., Bettica, P., Pich, EM., Jacobsson, E., Wahlstedt, K., Fredrikson, M., Furmark, T. (2012). Amygdala subregions tied to SSRI and Placebo response in patients with social anxie-ty disorder. Neuropsychopharmacology, 37(10):2222-2232.

IV Faria, V., Åhs, F., Linnman, C., Appel, L., Bani, M., Bettica, P., Pich, EM., Fredrikson, M., Furmark, T. (2012). Amygdala-prefrontal coupling tied to SSRI and placebo in the treatment of social anxiety disorder. Manuscript in preparation.

Reprints were made with permission from the respective publishers.

Contents

1. Introduction .......................................................................................... 11 1.1 Historical perspective on placebos ..................................................... 13

1.1.1 The slow birth of clinical trials ................................................... 13 1.1.2 Placebos as controls .................................................................... 14 1.1.3 Therapeutic placebos .................................................................. 16

1.2 Theoretical framework ....................................................................... 18 1.2.1 Conditioning ............................................................................... 18 1.2.2 Expectancies ............................................................................... 18 1.2.3 Conditioning vs. expectancies .................................................... 19 1.2.4 Reward model ............................................................................. 19 1.2.5 Meaning model ........................................................................... 20

1.3 Individual variability in placebo responses ........................................ 21 1.4 Neurobiology underlying placebo responses ...................................... 21

1.4.1 Placebos in pain .......................................................................... 22 1.4.2 Placebos in Parkinson’s disease .................................................. 23 1.4.3 Placebos in Affective disorders .................................................. 24

1.5 Anxiety - Social anxiety ..................................................................... 26 1.5.1 Epidemiology .............................................................................. 27 1.5.2 Personal and social burden .......................................................... 27 1.5.3 Etiology ....................................................................................... 27

1.6 Neurofunctional mechanisms underlying anxiety .............................. 29 1.6.1 The fear hub - Amygdala ............................................................ 30 1.6.2 Emotion regulation - Prefrontal cortex ....................................... 33

1.7 Serotonin and anxiety ......................................................................... 36 1.7.1 Imaging genetics ......................................................................... 37 1.7.2 SSRI treatment ............................................................................ 39

1.8 Social anxiety - Clinical placebo model ............................................. 41

2. Aims ..................................................................................................... 42

3. Methods ................................................................................................ 43 3.1 Clinical trials ....................................................................................... 43 3.2 Participants and recruitment ............................................................... 43 3.3 Treatment procedure ........................................................................... 45 3.4 Experimental public speaking task ..................................................... 45 3.5 Clinical behavioral measurements ...................................................... 46

3.5.1 Clinician-rated anxiety measures ................................................ 463.5.2 Self-report anxiety measures ....................................................... 46

3.6 Merging the groups ............................................................................. 473.7 Genotyping ......................................................................................... 473.8 Positron Emission Tomography ......................................................... 47

3.8.1 Scanner specifications ................................................................. 493.8.2 Pre-processing ............................................................................. 493.8.3 Statistical modeling and inference .............................................. 50

4. Summary of studies ................................................................................... 514.1 Study I ................................................................................................. 51

4.1.1 Background & aim ...................................................................... 514.1.2 Literature search .......................................................................... 514.1.3 Results ......................................................................................... 514.1.4 Conclusions ................................................................................. 52

4.2 Study II ............................................................................................... 524.2.1 Background & aim ...................................................................... 524.2.2 Results ......................................................................................... 534.2.3 Conclusions ................................................................................. 53

4.3 Study III .............................................................................................. 544.3.1 Background & aim ...................................................................... 544.3.2 Results ......................................................................................... 544.3.3 Conclusions ................................................................................. 55

4.4 Study IV .............................................................................................. 554.4.1 Background & aim ...................................................................... 554.4.2 Results ......................................................................................... 564.4.3 Conclusions ................................................................................. 57

5. General discussion ..................................................................................... 585.1 Placebos and PFC control ................................................................... 595.2 TPH2 gene as a biomarker for placebo anxiolysis ............................. 625.3 Anxiolytic subregional amygdala targets ........................................... 645.4 Amygdala-prefrontal anxiolytic couplings ......................................... 665.5 Limitations .......................................................................................... 695.6 Concluding remarks ............................................................................ 72

6. Acknowledgments ..................................................................................... 75

7. References ................................................................................................. 77

Abbreviations

ACC BLA BM BOLD CBT CCK Ce DA dACC DBS dlPFC dmPFC DSM DTI EEG FDA FDG fMRI GABA LA MEG MTL MNI NAc OFC PAG PD PET PFC rACC rCBF RCT

SAD SSRI TMS

Anterior cingulate cortex Basolateral amygdala Basomedial amygdala Blood oxygenation level dependent Cognitive behavioral therapy Cholecystokinin Central amygdala Dopamine Dorsal anterior cingulate cortex Deep brain stimulation Dorsolateral prefrontal cortex Dorsomedial prefrontal cortex Diagnostic and statistical manual of men-tal disorders Diffusion tensor imaging Electroencephalography Food and Drug Administration Fluorodeoxyglucose Functional magnetic resonance imaging Gamma amino-butyric acid Lateral amygdala Magnetoencephalography Medial temporal lobe Montreal Neurologic Institute Nucleus accumbens Orbitofrontal cortex Periaqueductal grey Parkinson’s disease Positron emission tomography Prefrontal cortex Rostral anterior cingulate cortex Regional cerebral blood flow Randomized control trial Social anxiety disorder Selective serotonin reuptake inhibitor Transcranial magnetic stimulation

TPH2 VLA vlPFC vmPFC 5-HTT 5-HTTLPR

Tryptophan hydroxylase-2 Ventrolateral amygdala Ventrolateral prefrontal cortex Ventromedial prefrontal cortex 5-hydroxy-tryptamine transporter Serotonin transporter gene linked poly-morphism region

INTRODUCTION

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1. Introduction

While it is well known that expectancies and beliefs shape experiences (Kirsch, 1999; Sterzer, Frith, & Petrovic, 2008), much remains to be learned about the neurobiological mechanisms behind this. A phenomenon that has proven to be fruitful in the investigation of this topic is the placebo effect1. The placebo effect can be defined as a beneficial outcome attributable to a treatment containing no pharmacodynamic or specific properties for the con-dition being treated, but in which patients believe in its effectiveness (Bene-detti, Mayberg, Wager, Stohler, & Zubieta, 2005). Expectancies seem to be at the heart of the placebo effect. Briefly, the study of this phenomenon rep-resents the study of how expectancies of improvement interact with distinct physiologic systems ultimately shaping mental and physical health (i.e., mind-body interactions).

In clinical trials the use of placebos has proven priceless in providing a baseline against which active new treatments are assessed. However, due to a growing placebo response, it has become more challenging to demonstrate superiority of active treatments over placebo (Alphs, Benedetti, Fleischhack-er & Kane, 2012; Marks, Thanaseelan, & Pae, 2009; Uhlenhuth, Matuzas, Warner, & Thompson, 1997; Walsh, Seidman, Sysko, & Gould, 2002). On the other hand, clinically relevant placebo effects have been reported for a variety of medical conditions including autoimmune and cardiovascular dis-eases, psychiatric, gastrointestinal and motor disorders, pain, asthma, demen-tia and addiction (Benedetti, 2009). Hence it is imperative, both from a methodological and a clinical perspective, to achieve a better understanding of how placebos exert their effects.

Neuroimaging techniques can provide invaluable contributions to our un-derstanding of the processes underlying this psychobiological phenomenon. It is nowadays possible to visualize and quantify changes in brain activity, neurotransmitters, and hormones elicited by placebo. Moreover, the growing field of imaging genetics opens new exciting opportunities to further investi-gate the biological markers that might account for individual variations in placebo responsivity. Even though placebos exert beneficial effects in sever-al medical conditions (Benedetti, 2009), the vast majority of placebo neuro-

1In this thesis the terms placebo effect and placebo response are used interchangeably as synonyms.

FARIA, V. (2012) MIND REALLY DOES MATTER

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imaging research stems from the field of pain (e.g., acute analgesic response in healthy subjects) and knowledge regarding the neural underpinnings of placebo responses beyond analgesia remains very limited. In order to move forward and translate the scientific knowledge into improved patient care, we need to further our understanding of how placebos exert their effects for a sustained period in clinical populations.

Social phobia, also known as social anxiety disorder (SAD), has been in-creasingly recognized as a chronic disabling and highly prevalent condition (Kessler, Chiu, Demler, Merikangas, & Walters, 2005; Yonkers, Dyck, & Keller, 2001) associated with great personal suffering, and high societal costs (François, Despiégel, Maman, Saragoussi, & Auquier, 2010). Clinical trials of SAD have shown that these patients benefit, to a moderately large extent, from placebo (Oosterbaan, van Balkom, Spinhoven, & van Dyck, 2001). Hence, SAD seems to constitute a good clinical model to investigate the placebo effect.

This thesis consists of four studies. The first study, a review, examines how functional neuroimaging research has contributed to the understanding of placebo across conditions. The following three empirical studies, explore the neurobiological underpinnings of sustained placebo response in a clinical population of SAD patients, and compare its neural and behavioral benefits with the first-line pharmacological therapy currently used for the treatment of this disorder, i.e., selective serotonin reuptake inhibitor (SSRIs).

The outline of these studies is preceded by a general introductory section on the placebo phenomenon, ranging from historical, theoretical, and mech-anistic perspectives supporting the therapeutic value of these responses. A background regarding epidemiology, neurobiology, and pharmacotherapy of SAD is also provided, paving the way for a summary of the empirical work, which is followed by a discussion section interpreting and integrating the findings.

Overall, I hope to provide some insightful answers and to motivate new research contributing to a better understanding of the mechanisms behind this phenomenon linking psychology, physiology, and clinical practice.

HISTORICAL PERSPECTIVE ON PLACEBOS

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1.1 Historical perspective on placebos “The cure for the headache was a kind of leaf, which required to be accom-panied by a charm, and if a person would repeat the charm at the same time that he used the cure, he would be made whole; but that without the charm the leaf would be of no avail.”

Socrates, according to Plato (Jowett, 1952)

Placebos have been an important component of healing throughout the histo-ry of medicine (Beecher, 1955; Wolf, 1959). Before randomized control trials (RCT), intrinsically inert substances or procedures were largely re-sponsible for the success of medicine. Early reports showed that countless treatments, once thought effective, were later found to be little more than placebos (for a detailed description see Shapiro & Shapiro, 1997). Back then, treatments were judged on the basis of pathophysiological rationales rather than comparative research (Feinstein, 1970) and the concept of place-bo was used for sham treatments given under the demand of pleasing rather than healing (Fox, 1803). See Box 1 for the etymology of placebo.

1.1.1 The slow birth of clinical trials

The first comparative clinical trial dates from the second half of the 18th century with James Lind’s investigation of the effects of various treatments for scurvy (Jaillon, 2007). In 1801, yet another important step was taken in comparative research by John Haygarth who reported the results of what might constitute the first medical placebo-controlled trial (de Craen, Kaptchuk, Tijssen, & Kleijnen, 1999). When comparing a commonly used therapy, known as Perkins tractors2, with a sham therapy (i.e., wood trac-tors), Haygarth showed that the sham procedure was as good as the therapeu-tic one. Interestingly, he seemed to have a clear notion concerning the signif-icance of the placebo effect when stating that “an important lesson in physic is here to be learnt, the wonderful and powerful influence of the passions of the mind upon the state and disorder of the body. This is too often over-looked in the cure of diseases” (de Craen et al., 1999). Together, these inves-tigations represent important early methodological steps taken towards evi-dence-based medicine. Nevertheless, the foundations of modern experimental medicine only started to be systematically outlined half a century later by Claude Bernard’s

2Consisted in applying metallic rods to the person’s body which were supposed to relieve the symptoms through the electromagnetic influence of the metal.


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classic revolutionary exposition of the scientific method, “founded on obser-vation and proved by experience”(Bernard, 1865/1949). This work advocat-ed the importance of scientific facts as the primordial guiders of medical knowledge, even if they contradicted generally accepted pathophysiological theories. Remarkably, the use of blind experiments was suggested as a way to ensure objectivity of scientific observations, revealing an early concern regarding the influence of suggestion and imagination. Bernard’s ideas strongly influenced the direction of medical experimentation (i.e., the use of scientific method in medicine), setting the stage for the evolution of method-ological concepts ultimately leading to the birth of RCTs. Even though the concept of clinical trials was not new, it was only during the 1930s that this term started to appear systematically in the medical litera-ture, establishing the basic principle of comparing several groups of patients undergoing different treatment regimens (Evans, 2004). By the first half of the twentieth century there was a growing acceptance for the use of these comparative methods in medical research. For the first time, the success of medicine was not confounded by placebo effects.

Box 1. Etymology The word placebo (i.e., Latin, for I shall please) entered the English language in the 13th century with a liturgical meaning “I will please the Lord [I will walk before the Lord] in the land of the living”. Five centuries later, we find the first documented proof of the medical use of the word placebo in the second edition of Motherby´s (1785) New Medical Dictionary introducing placebo as “a commonplace method or medicine”. This definition was revised in 1803 by Fox´s New Medical Dictionary now defining placebo as “an epithet given to any medicine adopted more to please than to benefit the patient”. With minor variations, this definition became commonly used during the 19th and the first half of the 20th centuries. Importantly, the first report limiting place-bos to inactive substances dates from the 1937 Taber´s medical dictionary: “Placebo, inactive substance given to satisfy patient´s demands for medicine; such as bread pills”. The introduction of the placebo term in a psychological dictionary appeared one decade later in Harriman´s New Dictionary of Psychology addressing placebos as “a pill or a liquid given to humor the patient with a psychoneurotic disorder. Its therapeutic effects, if any, are psychological, not physiologi-cal” (see Shapiro, 1968).

Due to the common use of placebos in medical research and clinical practice, current defini-tions of placebos and placebo effects are broader (see Moerman & Wayne 2002). Nowadays, placebos can refer to any form of treatment without a specific activity for the condition being treated. Placebo effects or responses, on the other hand, are not dependent on placebo admin-istration and placebo administration might not result in a placebo response. However, with or without placebos, placebo effects, commonly translated into psychological and physiological therapeutic benefits, depend on the significance and meaning of the intervention (i.e., conscious or unconscious expectancies of improvement) and are not a result of the active components of the treatment, spontaneous remission, or statistical biases.

1.1.2 Placebos as controls During the 1930s another refinement was added to modern clinical research – the placebo control. Studies introducing placebos as control procedures initially increased at a timid pace (Diehl, Baker, & Cowan, 1938; Evans &


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Hoyle, 1933; Gold, Kwit, & Otto, 1937). In the following years, randomiza-tion3 and the double-blind design4 also started to gain support. These refine-ments indicate that awareness regarding the importance and influence of suggestion and expectancies was emerging. At the time however, placebos were still vastly viewed as little more than a comfort for patients (Shapiro & Shapiro, 1997). In fact, it took more than two decades for clinicians to rec-ognize the therapeutic value of administering intrinsically inert treatments in control groups of trials. Also contributing to the growing interest and recognition of the placebo effect was the expanding influence of psychiatric concepts on medicine after the Second World War (Shapiro & Shapiro, 1997). Back then, researchers were starting to consider the importance of psychological factors, and place-bos seemed to be important therapeutic tools (Lasagna, 1956; Wolf, 1950, 1959). Among several significant publications was Henry Beecher’s land-mark paper “The Powerful Placebo” that played a major role in the recogni-tion of the clinical significance of placebos, advocating the scientific neces-sity of making randomized placebo-controlled clinical trials a standard pro-cedure (Beecher, 1955). Beecher, along with others, emphasized that all treatments, in addition to their specific effects, produce powerful placebo benefits. Therefore, in order to distinguish pharmacological effects from the effects of suggestion, the placebo response has to be subtracted from the therapeutic response. Sup-porting the claim that placebo effects had powerful consequences that should not be ignored in clinical trials were the reported estimations of placebo ef-fects that ranged from 26% to 58% (Beecher, 1955). Even though this influ-ential work lacked proper control-group comparisons, which has generated controversy (Hróbjartsson & Gotzsche, 2001, 2004; Kienle & Kiene, 1996, 1997), placebos became the gold standard methodological tool still valid in modern medical research. See Box 2 for a description of placebo confound-ing factors.

Also, crucial in this process was the decision made by the Food and Drug Administration (FDA) at the end of the 1970s requiring new drugs to be tested by randomized placebo-controlled trials before they could be licensed. Moreover, FDA recognition that researchers are also susceptible to sugges-tion and bias solidified the double-blind method that had resisted for many decades (Harrington, 2000). Nowadays, active placebos, that mimic the side effects of the active treatment under study, are also used to assure the blind-ness of clinical trials. Importantly, because these trials were designed to re-duce bias, over decades, placebos were mostly seen as background noise, which must be subtracted from active new treatments, rather than a positive

3Random allocation of patients into groups. 4Neither the participant, nor the physician knows which treatment is being administered.


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effect that could be exploited clinically. Hence, instead of a potential thera-peutic tool, placebos were regarded as an unwanted methodological conse-quence responsible for the failure of many clinical trials.

Box 2. Placebo confounding factors In 1997, Kienle & Kiene claimed that several factors might account for the outcome observed in the placebo group and in order to obtain an unbiased assessment of the placebo effect, changes observed in a no treatment control group should be subtracted from the placebo effect. Indeed, it seems more than fair that the same scientific principles applied to evaluate active treatments, should be considered when examining the efficacy of placebo responses. The confounding fac-tors are the following:

Spontaneous remission or natural history concerns the symptom fluctuation of a disease. Some conditions might spontaneously relapse without any intervention.

Regression towards the mean is a statistical principle noticeable when there are repeated measurements that tend to be closer to the mean on subsequent assessments. This is more evident when the selection of participants is based on extreme scores. If a patient’s disease is peaking in its intensity, while being enrolled in the treatment, a regression towards the mean predicts that on subsequent assessment an improvement will occur.

Response bias is a cognitive bias often associated with self-report measurements, influenced by patients’ perception of how they are expected to behave, which in turn is also influenced by the costs and the benefits of the answer. In this case, changes occur simply because the subject is under study (e.g., the Hawthorne effect).

Realizing that these confounding factors were commonly misplaced under the label of placebo, Hróbjartsson & Gotzsche (2001, 2004) conducted meta-analytic studies to estimate the specific power of the placebo effect i.e., by excluding the effects observed in the no treatment control groups. The authors suggested there is little evidence in general that placebos have powerful clinical effects. Their work, however, was not flawless and a wave of studies challenged their findings (Greene et al., 2001; Vase, Riley, & Price, 2002; Wampold, Minami, Tierney, Baskin, & Bhati, 2005). Evidence currently provided by clinical and experimental research studies, con-trolling for these confounds, have redefined placebos as interventions capable of producing clini-cally meaningful benefits (Benedetti et al., 2005).

1.1.3 Therapeutic placebos

During the last decades of the twentieth century, researchers started to per-form experiments comparing the magnitude of placebo effects. While using different dosages and different administration methods, these studies showed interesting variations in the placebo response (de Craen et al., 1999; de Craen, Tijssen, de Gans, & Kleijnen, 2000; Greene et al., 2001). For in-stance, four placebos seem more effective than two (Moerman, 2000), branded tablets produce more relief that unbranded ones (Branthwaite & Cooper, 1981), and green tablets seem to be twice as effective as red or yel-low in reducing phobic symptoms (Evans, 2004), even though all tablets contained exactly the same active compound. There is also compelling evi-dence showing that injected placebo is more effective than oral placebo (de Craen et al., 2000), but nothing seems better than placebo surgery and expo-sition of patients to technically sophisticated equipment (Evans, 2004). Hence, if placebos have no effect beyond the statistical artifacts, changing


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type, form, color or quantity would make no difference in the clinical out-come. It seems, however, that all these factors play a substantial role in in-fluencing the significance and meaning of the treatment (i.e., expectancies), ultimately shaping the clinical outcome.

The open-hidden paradigm5 has been an invaluable methodological tool allowing the investigation of the magnitude of the placebo effect during the administration of active treatments in clinical conditions without ethical constrains. In the treatment of postoperative anxiety, a clear decrease in anx-iety was noted in the open group, but not in the hidden administration group when comparing open and hidden administration of 10mg diazepam (benzo-diazepine). Hence, anxiety relief observed after diazepam was suggested to reflect a placebo effect (Colloca, Lopiano, Lanotte, & Benedetti, 2004). Comparisons between the open and hidden administrations have also been performed for distinct painkillers and the results show that the needed anal-gesic dose to reduce pain was much higher with hidden than for open infu-sions (Benedetti et al., 2003; Colloca et al., 2004). Other studies have no-ticed that hidden administrations of 6-8 mg of morphine correspond to saline administration when expectancies of analgesia were openly induced (Levine & Gordon, 1984). Likewise, therapeutic advantages of open over hidden treatments have been reported for Parkinsonian patients (Colloca et al., 2004).

Also supporting the importance of expectancies, but this time in placebo-induced therapeutic outcomes, are the findings of a meta-analytic study no-ticing a greater magnitude of placebo responses in experimental settings, where manipulation of expectancies takes place, in comparison to clinical-controlled studies, where explicit oral suggestions of improvement are avoided (Vase et al., 2002). Overall, these studies suggest that the infor-mation provided by the context surrounding the treatment has a meaningful therapeutic effect that cannot be disregarded.

A paradigm shift occurred in the beginning of this century whereby pla-cebo effects were transformed from nuisance factors in clinical trials to tar-gets of scientific investigation. Aware of potential contaminating factors previously misplaced under the label of placebo, researchers are nowadays, with the help of neuroimaging tools able to achieve remarkable scientific accomplishments in this field (Benedetti et al., 2005). Questions regarding the placebo phenomenon have been reformulated and instead of inquiring whether or not placebo responses are real, the emphasis is now placed on the mechanisms behind these responses.The next section provides a brief theo-

5Any pharmacological treatment has both a specific pharmacodynamic and a placebo psycho-logical component - the therapeutic effect is the sum of these. In an open-hidden paradigm, the effectiveness of the pharmacological treatment is evaluated by eliminating treatment expectations through a hidden administration of the drug (Levine & Gordon, 1984).


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retical overview of the most common conceptions addressing the question of how placebos work, thus setting a theoretical stage for the mechanistic neu-rofunctional findings.

1.2 Theoretical framework 1.2.1 Conditioning Seen as conditioning, the placebo effect can be explained as a phylogenet-ically general behavioral phenomenon. Early findings of placebo effects in animals (Ader & Cohen, 1975, 1982; Herrnstein, 1962) suggested that pla-cebo responses in humans might be nothing more than Pavlov conditioning. After repeated associations, aspects of the clinical setting related to clinical improvement can result in a clinical benefit in the absence of the active ther-apeutic component (Siegel, 2002; Wickramasekera, 1980). During condi-tioning, a pharmacological active component is associated with a neutral stimulus, which might be an inactive pill form. After conditioning, the inac-tive form alone triggers the therapeutic response.

This type of associative learning might constitute the basis of many pla-cebo responses. Even though a conditioning model might be too simplistic to be generalized to all placebo responses, placebos administered after previous conditioning are more effective than when administered for the first time (Amanzio & Benedetti, 1999; Wager & Nitschke, 2005). Moreover, it has been shown that placebo responses get stronger with increasing number of paired associations (Phil & Altman 1971). Evidence of conditioned placebo responses has been documented in both animal (Ader & Cohen, 1982; Exton et al., 1998, 1999, 2000) and human (Benedetti et al., 2003; Goebel et al., 2002; Goebel, Meykadeh, Kou, Schedlowski, & Hengge 2008) studies. Therefore, an interesting feature of the classical conditioning model is the automatic, unconscious aspect which allows a generalization of this response across different species.

1.2.2 Expectancies Classical conditioning theory in itself cannot account for the fact that place-bo responses can occur without previously experiencing the drug. The ex-pectancy theory, on the other hand, emphasizes the importance of expectan-cies and postulates that placebos produce a clinical effect because the receiv-er expects it to do so (Gladstein, 1969; Kirsch, 1990). Accordingly, expecta-tions of improvement are the key factor in placebo-induced benefits. Other cognitive factors such as decrease in self-defeating thoughts (Stewart-Williams & Podd 2004), motivation (Price, Finniss, & Benedetti, 2008),

THEORETICAL FRAMEWORK

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faith (de la Fuente-Fernández & Stoessl, 2002), and meaning of the illness experience (Brody & Brody, 2000) might mediate expectancy formation. The majority of research on placebos has focused on expectations as the crucial factor triggering the placebo responses. The significance of expec-tancies has been illustrated in studies showing that expectancies may over-ride active pharmacological effects (Kirsch, 1990). An apparent distinction between this theory and the conditioning theory is that expectancy effects seem to be dependent on the participants’ state of mind (i.e., conscious awareness), whereas conditioning responses are not.

Even though expectancy-induced placebo responses generated by verbal suggestions can be seen as conceptually distinct from conditioned placebo responses, these models do not have to be mutually exclusive; they can be seen as having complementary properties (Stewart-Williams & Podd, 2004).

1.2.3 Conditioning vs. expectancies Current research on placebo effects is inspired by both conditioning and expectancy theories. Because what is learned in Pavlovian conditioning is expectation (Bootzin, 1985), expectancy and conditioning are probably act-ing together in achieving clinical benefit. Accordingly, it has been docu-mented that the magnitude of the placebo response is enhanced when expec-tancy and conditioning are combined (Amanzio & Benedetti, 1999).

Studies trying to disentangle the psychological mechanisms underlying placebo responses have shown, however, that verbally induced suggestions and conditioned learning can result in distinct responses (Benedetti et al., 2003). Nevertheless, verbally induced suggestions and conditioning should be seen as vehicles through which expectations are acquired. Although there is probably a multitude of factors affecting the formation of expectancies in clinical settings, it is likely that both verbally induced suggestions and condi-tioning contribute to a therapeutic effect. Reward expectation is an interest-ing perspective proposing a common biological pathway for expectancy-induced placebo responses.

1.2.4 Reward model Similar to the expected beneficial outcomes of placebo, rewards are usually directed to increasing survival. The reward system is thought to promote survival of species by rewarding survival behaviors. As mentioned earlier, before RCTs, when most therapeutic substances were lacking the specific pharmacodynamic properties, placebos might have played an important role in survival (de la Fuente-Fernández & Stoessl, 2004). According to this model, expecting a clinical benefit is a form of reward that might trigger the placebo response (de la Fuente-Fernández & Stoessl, 2002; Lidstone & Stoessl, 2007).


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The reward system was discovered almost six decades ago when Olds and Milner (1954) noticed that a rat would persistently press a lever to receive weak electric stimulation when an electrode was implanted in the nucleus accumbens (NAc). They concluded that stimulation of the accumbens was rewarding for the animal and that this adaptive system was capable of modu-lating behavioral responses. Since then, dramatic advances have been achieved in this field and current research on reward circuitry shows that the NAc plays a central role in dopamine (DA) mediated reward mechanisms responding to the magnitude of the anticipated rewards and deviations from the predicted outcomes (Lidstone et al., 2010; Setlow, Schoenbaum, & Gal-lagher, 2003; Tobler, Fiorillo, & Schultz, 2005). Other brain regions such as the ventral tegmental area of the midbrain (containing the cell bodies of the mesolimbic system projecting primarily into the NAc), the amygdala, peria-queductal grey (PAG), frontal, and cingulate cortices have been reported to be important pieces of this circuitry (de la Fuente-Fernández & Stoessl, 2002). Recent neurochemical and neurofunctional findings (see Section 1.4) support this model as a probable explanation of the mechanisms underlying expectancy-induced placebo responses.

1.2.5 Meaning model A broader way of understanding the placebo effect was suggested by the anthropologist Daniel Moerman (2002). Moerman argued that the term pla-cebo effect might be confusing because it includes aspects that have nothing to do with placebos. Whereas placebos clearly cannot do anything, their meaning certainly can. Thus, to fully comprehend this complex phenome-non, it was proposed that the focus should rather be on the psychosocial and contextual factors affecting the individual meaning of the treatment. The importance of studying the symbols and the meaning of the contextual fac-tors surrounding the treatment, which affect expectations of relief, is at the core of this perspective. It provides a valuable contribution emphasizing a broader dimension of the placebo response, beyond the traditional placebo inactive perspective. In light of this model, contextual factors, that were not considered previously, can now add to the explanation of why placebo re-sponses vary between patients and situations.

It is important, however, to emphasize that the presented theories should not be viewed in opposition; rather they provide different and complemen-tary contributions by focusing on different aspects of this complex response. For instance, the meaning of the treatment, which is influenced by psychoso-cial and contextual factors, might influence conscious or unconscious expec-tancies of relief that in turn can trigger the reward system ultimately affect-ing the individual therapeutic outcome.

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1.3 Individual variability in placebo responses The idea that certain individuals are more prone to respond to placebos is not new. As implied above, individual differences are likely determined by the combination of situational and dispositional factors. With regard to disposi-tional factors, early studies investigating placebo-prone psychological pro-files have, however, provided unreliable results (Geers, Helfer, Kosbab, Weiland, & Landry, 2005). Nevertheless, recently, the personality variable dispositional optimism has been consistently associated with a greater re-sponse to positive expectations (Morton, Watson, El-Deredy, & Jones, 2009). Moreover, individual differences in the efficiency of the reward sys-tem were also shown to predict individual variations in placebo response (Scott et al., 2007). Also, a compromised communication between the pre-frontal cortex (PFC) and the rest of the brain might underlie a reduced place-bo response (Benedetti et al., 2006). A more detailed description of these findings is provided in the next section - 1.4. It seems that placebo responses can be reproducible when environmental cues remain consistent, supporting the existence of stable individual differences.

Surprisingly this topic still receives little attention. Identifying genetic, neurofunctional, or psychological predictor variables for placebo responders, however, has important implications that should not be disregarded. From a clinical perspective, the identification of patients who are most likely to re-spond therapeutically to placebo enables an application of this knowledge to tailor treatments to patients’ specific needs. From a methodological perspec-tive, this information would also allow a control for the variance in clinical trials. Because this thesis is oriented more towards a biological and mecha-nistic approach to how placebos produce anxiolytic effects in SAD, a por-trayal of previous placebo neurobiological findings is in order.

1.4 Neurobiology underlying placebo responses Blood flow, metabolic, electric and magnetic changes, neuroreceptors and neurotransmitters are some of the features underlying placebo response that can be explored with brain imaging techniques (see Box 3 for a brief descrip-tion of commonly used imaging techniques). These techniques enable an objective measurement of the underlying placebo-related beneficial process-es that cannot be readily assessed by mere observations or self-reports. With these tools it can be demonstrated that placebos not only affect behavior, but also activity in disorder-specific neural pathways.

Although neither pain, nor Parkinson’s disease (PD) is the main focus of this thesis, the majority of placebo imaging research has been carried out


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within these fields. Moreover, a general modulatory network for placebo responsivity has been suggested (e.g., Petrovic et al., 2005; Scott et al., 2007), justifying the inclusion of these findings.

Box 3. Common imaging techniques Positron emission tomography (PET) can be used to measure regional cerebral blood flow (rCBF), glucose metabolism and characteristics of neurotransmitter systems by means of posi-tron emitting radionuclides incorporated into a variety of biological active molecules (radiotrac-ers). Due to its versatility and ability to probe biochemical pathways and metabolic levels, PET is the most powerful molecular imaging technique.

Functional magnetic resonance imaging (fMRI) on the other hand, due to its availability, good spatial and temporal resolution and lack of radiation exposure is the preferred method for probing neural activation patters. Like rCBF, blood oxygenation level dependence (BOLD) is an indirect measure of neural activity. PET and fMRI are used to generate maps reflecting regional brain activity during rest and in response to challenges. Both techniques allow measurements of cortical and subcortical alterations.

Electroencephalography (EEG), although more restricted to cortical areas in comparison to PET and fMRI, provides a direct measure of the brains electrical activity with an excellent tem-poral resolution. Magnetoencephalography (MEG), as EEG, allows direct access to the activity of cortical neurons but through magnetic fields originated by the electrical activity of the brain.

Intraoperative neurophysiological techniques enable a detailed mapping of physiological func-tions by means of microstimulation and microrecording from single neurons. Deep brain stimu-lation (DBS), an invasive technique, involves a surgical implantation of a neurostimulator that sends electrical impulses to specific brain areas. A noninvasive technique also allowing the stim-ulation of specific brain areas is Transcranial magnetic stimulation (TMS).

1.4.1 Placebos in pain The systematic interest for the placebo phenomenon was born in the field of pain (Beecher, 1955), and even today placebo analgesia is by far the most studied type of placebo response (Benedetti, 2009). Psychosocial regulation of pain together with the possibility to induce pain experimentally makes pain a good model for assessing placebo responses. Three decades ago, Levine, Gordon and Fields (1978) reported that nalox-one (i.e., mu-opioid antagonist) reversed expectancy-induced placebo anal-gesia in placebo responders as compared to nonresponders. Further investi-gations in placebo analgesia showed that proglumide6 (i.e., Cholecystokinin [CCK] antagonist) doubled the placebo analgesic response (Benedetti, Amanzio, & Maggi, 1995). This work was complemented by a set of exper-iments showing that placebos can also reduce pain through non-opioid mechanisms (i.e., naloxone-irreversible) when preceded by conditioning with non-opioid drugs (Benedetti, Arduino, & Amanzio, 1999).

Concerning the central brain mechanisms of endogenous opioid release, PET studies using the radiotracer carfentanil, a mu-opioid agonist, supported

6Proglumide has been shown to enhance analgesia resultant from opioid drugs (McCleane, 2003).

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the involvement of the opioidergic system, in placebo analgesia (Wager, Scott, & Zubieta, 2007; Zubieta et al., 2005). The importance of this modula-tory descending circuitry was clarified by a pharmacological functional magnetic resonance imaging (fMRI) study showing that naloxone antago-nized both behavioral and neural placebo effects by abolishing the rostral anterior cingulate cortex (rACC)-PAG coupling (Eippert et al., 2009a), orig-inally reported by Petrovic, Kalso, Petersson and Ingvar (2002).

Evidence supporting the suggestion that nociceptive processing is inhibit-ed at an early stage is also provided by electroencephalography (EEG) and magnetoencephalography (MEG) studies (Lorenz et al., 2005; Wager, Matre, & Casey, 2006). Moreover, findings point towards an inhibition of the noci-ceptive placebo responses at the level of the spinal cord (Eippert, Finster-busch, Bingel, & Büchel, 2009b; Matre, Casey, & Knardahl, 2006). Later components, however, have also been reported (Wager et al., 2006). Fur-thermore, stronger activations (i.e., lateral orbitofrontal cortex [OFC] and ventrolateral prefrontal cortex [vlPFC]) and connectivity (i.e., between the lateral OFC and rostral ACC) patterns were recently observed during place-bo analgesia when compared to remifentanil, an opioid agonist, suggesting a unique placebo mechanism that might not be required in remifentanil-induced analgesia (Petrovic et al., 2010). Hence, it seems reasonable to state that a variety of placebo analgesic responses exist engaging both opioidergic and non-opioidergic mechanisms.

The dopaminergic system has also been shown to have a crucial role in the development of placebo analgesia. A substantial proportion of the varian-ce in placebo analgesic responses has been linked to the capacity to activate the NAc in response to rewards. Participants with the greatest hemodynamic and neurochemical NAc responses showed the most profound placebo anal-gesic responses (Scott et al., 2007). Moreover, greater DA and opioid activi-ty in NAc accompanied placebo analgesic responsiveness, whereas nocebo responses7 were related to a reduction in both neurotransmitters (Scott et al., 2008). These findings suggest that intrinsic differences in the reward system might predict individual variations in placebo analgesia responsivity.

Overall, placebo analgesia studies provide evidence of how the admin-istration of an otherwise inactive agent, a placebo, is capable of modulating pain processing.

1.4.2 Placebos in Parkinson’s disease Placebo research on PD has allowed us to expand our knowledge of both general and specific (i.e., condition related) mechanisms underlying placebo responses. PD is characterized by a progressive degeneration of DA neurons

7Expectations of a negative outcome that might lead to worsening of symptoms.


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in the nigrostriatal pathway resulting in debilitating motor dysfunctions. Improvements in PD can be assessed more objectively by a blinded examin-er because, unlike pain, it does not have the methodological disadvantage of being subjectively evaluated by means of self-reports.

As in placebo analgesia, expectations of symptom improvement seem to modulate the brain neurochemistry in PD. Studies report the involvement of an endogenous release of DA in the nigrostriatal system (e.g., de la Fuente-Fernández et al., 2001). Moreover, placebo-induced DA release in the NAc of PD patients was related to expectancies of clinical benefit suggesting that DA might contribute to the placebo response by influencing the reward cir-cuitry (de la Fuente-Fernández, & Stoessl, 2002). These findings were complemented by a PET study showing that the strength of expectancies of clini-cal improvement modulated striatal dopaminergic neurotransmission (Lid-stone et al., 2010).

Placebo administration was also found to decrease the activity of subtha-lamic neuronal discharge in Parkinsonian placebo responders (Benedetti et al., 2004). These changes in single neuron subthalamic activity were highly correlated with a reduction in upper-limb rigidity. Moreover, opposite expec-tations of either bad or good motor performance modulate the therapeutic effects of subthalamic nuclei stimulation in a fast way (Pollo et al., 2002). It seems that the change in subthalamic nucleus neuronal firing is a down-stream effect of placebo-induced DA release in the dorsal striatum.

It was after the initial report of endogenous release of DA in the NAc of PD patients that the reward system was hypothesized as one of the probable mechanisms mediating expectancy-induced placebo responses across disor-ders and conditions (de la Fuente-Fernandez, & Stoessl, 2004).

1.4.3 Placebos in Affective disorders Affective disorders entail a spectrum of conditions typically characterized by a pervasive alteration in mood, affecting thoughts, emotions and behaviors. Belonging to this group, depression and anxiety disorders share several fea-tures and usually respond to the same type of pharmacological treatments (SSRIs being the most commonly used). Even though depression and anxiety disorders share the methodological disadvantage of being subjectively eval-uated, as with pain, the increasingly high placebo response rate (Walsh, Seidman, Sysko, & Gould, 2002) constitutes both a clinical and a methodo-logical challenge that cannot be ignored.

In a controversial meta-analysis conducted on antidepressant clinical trials of major depression (2,318 patients), it was concluded that a quarter of the changes observed when administering an active compound were due to the specific action of the compound, another quarter was explained by con-founding factors (e.g., natural history of the disorder) and the remaining half was attributed to the placebo response (Kirsch & Sapirstein, 1998). In an

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attempt to further investigate and disentangle placebo-induced from pharma-logical-induced antidepressant changes in patients with major depression, Leuchter, Cook, Witte, Morgan, and Abrams (2002) reported a differential increase in frontal areas uniquely associated with placebo response by means of quantitative EEG. Placebo responders were also later found to have en-hanced cognitive processing speeds in a variety of neuropsychological tests (Leuchter et al., 2004). Even though placebo and pharmacological treatments were clinically indistinguishable, PFC changes observed only in placebo responders partially support the previously described analgesic findings (Krummenacher, Candia, Folkers, Schedlowski, & Schönbächler, 2010). This suggests that placebo response is dependent on PFC modulatory activi-ty regardless of conditions.

In an Flurodeoxyglucose (FDG) PET study of unipolar depression, May-berg et al. (2002) showed that placebo produced similar neural changes to those induced by the pharmacological treatment (i.e., SSRI-fluoxetine), sug-gesting a possible involvement of serotonin in placebo-induced antidepres-sant responses. However, no unique regional metabolic changes associated with placebo response were observed, whereas unique metabolic subcortical and limbic changes were reported for SSRI. Moreover, after 1 week of SSRI and placebo treatment, metabolic increases were observed in the ventral striatum and OFC in treatment responders (i.e., both SSRI and placebo), possibly reflecting an anticipatory reward-related antidepressant effect. This is in keeping with previously reported brain imaging studies in which the involvement of the ventral striatum, more specifically the NAc, was associ-ated with placebo analgesia (Scott et al., 2007) and placebo-induced motor improvement in Parkinsonian patients (de la Fuente-Fernández et al., 2001). Early metabolic changes observed in depressive patients therefore suggest that the reward circuitry might also play an important role in placebo-induced antidepressant responses. It remains unclear, however, whether pla-cebo-induced responses share a common or have a distinct pathway in com-parison with pharmacological-induced antidepressant responses.

Knowledge regarding the underlying neural mechanisms of placebo-induced anxiety relief comes from an fMRI study investigating how place-bos can modulate emotional perception during processing of emotionally unpleasant visual stimuli in healthy participants (Petrovic et al., 2005). Par-ticipants were initially conditioned with either midazolam, a benzodiazepine, which reduced the experience of unpleasantness, or flumazenil, a benzodiaz-epine receptor antagonist, which had the opposite effect. During emotional processing of unpleasant stimuli, placebo-induced anxiety relief resulted in activation of rACC and OFC. These areas have been previously reported as key modulatory regions in placebo analgesia. Participants with larger expec-tations showed the largest changes in emotional regulatory areas. Moreover, previously induced treatment expectations were correlated with placebo-induced activations of the ventral striatum which is consistent with the pla-


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cebo reward model in an emotion regulation context. Interestingly, this study not only supports a reward-based general modulatory process in placebo responses, but it also shows how emotional regulation which is a core issue in affective disorders can be investigated experimentally.

In line with previous findings, these studies suggest that placebo antide-pressive/anxiolytic responses seem to be modulated by areas in the PFC (Leuchter et al., 2002; Petrovic et al., 2005). Expectations seem to play an important role and the involvement of the ventral striatum possibly reflects an anticipatory reward-related antidepressant/anxiolytic effect. These studies support the reward circuitry as a possible general underlying mechanism triggering placebo responses. Interestingly, data on depression also suggests that serotonin might be involved in placebo-antidepressant responses.

Even though clinical trials have shown strong placebo responses in affec-tive disorders (Walsh et al., 2002), only a few parts of the underlying benefi-cial mechanisms have been revealed. With regard to SSRIs and placebo mechanisms, the results are somehow contradictory, and are not able to re-solve the controversy surrounding commonly used pharmacological treat-ments for both depression and anxiety disorders (Kirsh & Sapirstein, 1998).

Impelled by the lack of knowledge regarding sustained clinical placebo responses in anxiety, the empirical work presented in this thesis is based on placebo-induced anxiolysis in patients with SAD. Hence, the next section introduces an epidemiological, clinical, and neurobiological description of SAD presented as a vehicle to explore and expand our knowledge about this mechanistically intriguing phenomenon.

1.5 Anxiety - Social anxiety Everybody knows what it is like to feel anxious, and almost everyone will probably admit some anticipatory anxiety in situations such as giving a speech. Expressions of fear and anxiety are considered normal reactions in the imminence of danger. For some individuals, however, the fear goes be-yond adaptive and develops into a pathological state interfering with normal functioning. This is the case with psychiatric anxiety disorders. Character-ized by sustained states of apprehension without an objective environmental threat, anxiety disorders comprise heterogeneous conditions such as general-ized anxiety disorder, obsessive-compulsive disorder, panic disorder, phobi-as, posttraumatic stress disorder and social anxiety disorder. SAD, also known as social phobia, arguably the most common anxiety disorder (Jeffer-ys, 1997), is the focus of this thesis.

ANXIETY – SOCIAL ANXIETY

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1.5.1 Epidemiology Epidemiological work shows that anxiety disorders are the most common mental health disorders associated with a high lifetime prevalence of 28.8% (Kessler et al., 2005). They cause pronounced personal suffering (Comer et al., 2011) and high societal costs (François et al., 2010). Characterized by an excessive fear of being observed or scrutinized by others, when facing social situations (APA, 2000), SAD is among the most common of all mental dis-orders with 12.1% lifetime prevalence (Kessler et al., 2005). Community epidemiological work estimates that more than 12% of the North American population fulfill the criteria for SAD at some point in their lives (Kessler et al., 2005), with similar or even higher prevalence rates reported for the Swe-dish population (Furmark et al., 1999).

1.5.2 Personal and social burden Persons with SAD are mostly concerned with how they are perceived by others. The excessive fear underlying SAD is translated into agony of per-forming inadequately in social situations. When exposed to the feared situa-tions, anxiety symptoms such as palpitations, sweating, blushing and a tor-rent of negative thoughts are common responses. These responses may occur during and in anticipation of social situations. Making friends, casual con-versations, dating, applying for jobs, or speaking in front of an audience are either avoided or endured with intense anxiety. This interferes considerably with the person’s life (Fehm, Pelissolo, Furmark, & Wittchen, 2005). Public speaking is the most prevalent social fear and significant performance fears in these situations are experienced by between 15 and 39% of the normal population (Furmark et al., 1999).

SAD constitutes both a personal and social burden (François et al., 2010). Individuals with SAD often do not seek treatment for many years or until they have developed a secondary disorder (Fehm et al., 2005). When un-treated, SAD tends to have a chronic course and it is unlikely to remit spon-taneously (Yonkers et al., 2001). The development of comorbid disorders plus the severity of SAD and the chronic course increases the overall burden of the condition for both individuals and society at large (Schneier et al., 2010).

1.5.3 Etiology SAD typically starts between early and late adolescence, with 80% of the cases occurring before the age of 18 years (Otto et al., 2001). Like many psychiatric disorders, SAD is likely to be multicausal and whereas its etiolo-gy is yet far from being fully explained important contributions have been made to understand the factors related to its origins. Biological, environmen-


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tal and psychological factors play an important role in the pathogenesis of this complex disorder. A large body of research investigating temperamental factors has linked behavioral inhibition and SAD (Hirshfeld-Becker, 2010). Family environment can also affect the likelihood of developing this disor-der (Beidel & Turner, 1998).

There is a familial aggregation of shyness, social anxiety, and SAD (Hirshfeld-Becker, 2010). The fact that this disorder runs in families not only suggests an important family role in the behavioral etiology, but also hints at a genetic contribution. The contribution of genetic factors is as high as 51% (Hettema, Neale, & Kendler, 2001). Nonshared environmental influences, however, are also particularly salient for SAD (Hallett, Ronald, Rijsdijk, & Eley, 2009), suggesting that what is genetically inherited is a broader predis-position that influences or moderates the relation between environmental risk and psychopathology.

Classical conditioning models suggest that SAD might result from asso-ciative learning in social traumatic events (Mineka & Zinbarg, 1995). Cogni-tive models (Clark & McManus, 2002; Rapee & Heimberg, 1997) highlight the importance of emotional enhanced reactivity, which is thought to arise from distorted appraisal of social situations. These cognitive distortions transform inoffensive social cues into personal threats resulting in a distor-tion of the self, as social incompetent, and a distortion of others, as critical judges. Neuroimaging evidence supports this cognitive failure to regulate negative emotional reactivity as an important mechanism underlying SAD (Goldin, McRae, Ramel, & Gross, 2009).

As suggested above, it is unlikely that the etiology of SAD is due to a single factor or mutually exclusive factors. On the contrary, a complex inter-action between these factors is thought to result in the exaggeration of emo-tional apprehension which is the essence of this pathology. Neurofunctional-ly, the circuits underlying fear are thought to be critical for SAD. Briefly, hyperactivation of limbic brain structures (particularly the amygdala), to-gether with hypoactivation of cortical emotional regulation structures (par-ticularly the medial PFC) seem to exaggerate and maintain these fears (Phelps, Delgado, Nearing, & LeDoux, 2004).

Genetic factors have been shown to play an important role in the neural circuitries and behavioral responses underlying SAD (Furmark et al., 2004). Individuals with a genetic predisposition for overly reactive fear circuits might be more vulnerable to psychosocial stressful factors. On the other hand, early stressful events might trigger neurodevelopmental consequences affecting latter responses (Rosen & Schulkin, 1998). Either way, investigat-ing these anxious neurobiological responses becomes crucial for understand-ing the development and treatment of SAD, whether resulting from learning, stressful experiences, or genetic predispositions. A neurobiological perspec-tive of emotion regulation in anxiety is further discussed below.

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1.6 Neurofunctional mechanisms underlying anxiety As previously suggested, fear is a key element of anxiety disorders. Hence, it is not surprising that studies regarding the neural basis of anxiety have their roots in fear circuits in nonhuman models (LeDoux, 2000). These models have provided crucial information regarding the neurocircuitry associated with fear responses. Due to its invasive nature, however, the methods used in animal research (e.g., lesion and tracing studies) are difficult to employ in humans. Human neuroimaging research has, nevertheless, been able to con-firm that the basic components of the fear circuitry are well preserved across species. In Box 4 a brief description of the neurocircuitry that is thought to mediate these emotional responses is provided.

Box4. The neuroanatomy of anxiety Fearful relevant stimuli are thought to reach the amygdala either directly or indirectly. Via the highroad, the sensory systems pass the potentially dangerous information from peripheral receptor cells to the dorsal thalamus, which relays this information to primary sensory areas in the cortex. The stimulus is further processed by neighboring cortical regions that in turn project to widespread re-gions of the brain including the cingulate, orbitofrontal cortex (OFC) and amygdala. However, di-rect projections also reach the amygdala from the sensory thalamus. This low road route processes the stimuli independent of conscious awareness, leading to a rapid but imprecise physiological re-sponse to threatening situations. The hippocampus receives inputs from all sensory systems via transition areas such as entorhinal, perirhinal and parahippocampal cortices, and it is interconnect-ed with the amygdala. The amygdala in turn projects to the periaqueductal gray (PAG), the stria-tum and hypothalamus resulting in physiological and behavioral responses. Cortical regions such as dorsal anterior cingulate cortex (dACC) and dorsomedial prefrontal cortex (dmPFC) are in-volved in more extensive evaluation of emotion and may gate the stimulus access into conscious awareness. The rostral ACC together with the subgenual ACC and the ventromedial prefrontal cor-tex (vmPFC) are involved in contextually suitable regulation of emotion and limbic processing. The lateral prefrontal cortex (lPFC) might involve the medial PFC emotion processing circuitry to assist in voluntary emotion regulation (see Hartley & Phelps, 2010; LeDoux, 2000).

Known for its crucial role in fear processing and conditioning (LeDoux, 2000), the amygdala, the most consistent structure reported in the neuropa-thology of anxiety (Shin & Liberzon, 2010), represents a good starting point. The amygdala does not act independently; on the contrary, it is embedded in multiple limbic cortical networks. Medial prefrontal regions (mPFC), known for their important role in emotional regulation (Davis and Whalen, 2001; Oschner, Bunge, Gross, & Gabrieli, 2002), are thought to be crucial for the development and/or maintenance of anxiety disorders. Considering that the processing of emotional stimuli is abnormal in SAD (i.e., with increased negative and threat-relevant processing), examining the connectivity be-tween the amygdala and these prefrontal regions seems crucial for a deeper understanding of the underlying mechanisms behind this disorder. Interac-


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tions between top-down and bottom-up8 mechanisms might determine be-havioral adaptions and underlie anxiety conditions (Kim et al., 2011). In fact, a disrupted functional connectivity between the amygdala and the PFC has been shown in subjects with SAD (Ding et al., 2011; Hahn et al., 2011). Hereafter, besides the amygdala, particular attention will be given to the PFC and the connectivity between these cortical and subcortical subre-gions - Section 1.6.1 & 1.6.2. Apart from the amygdala and the PFC subregions, other areas like the insula, hippocampus, hypothalamus and brainstem, known to be part of the fear network, are also involved in the development and maintenance of anxi-ety disorders. However, a detailed description of these regions falls beyond the scope of this thesis.

1.6.1 The fear hub - Amygdala

Anatomical organization Located deep within the anterior medial temporal lobe (MTL), the amygdala is a complex structure composed of a collection of distinct subnuclei with complex interconnections (LeDoux, 2007). Based on dissimilarities in cytoarchitecture, myeloarchitecture and chemoarchitecture, researchers have long challenged the traditional view of this region as a functional-anatomical instance (Swanson & Petrovich, 1998). A commonly accepted classification scheme differentiates the amygdala into laterobasal (i.e., lat-eral, basolateral, basomedial, and basoventral nuclei), superficial (i.e., corti-cal) and centromedial (i.e., central and medial nuclei) subgroups (Heimer et al., 1999) - Figure 1.

Connectivity Regarding amygdala’s connectivity, the majority of cortical and subcortical inputs converge in the laterobasal subdivision, more specifically in the lat-eral subnuclei, which is thought to be the major amygdala input area (i.e., converging sensory thalamic and cortical inputs). The basolateral nucleus, on the other hand, is thought to comprise dense reciprocal connections with OFC and medial prefrontal cortex, playing an important role in emotional regulation (Kim et al., 2011).

8Automatic responses to threat are viewed as bottom-up, whereas subsequent regulatory re-sponses refer to top-down. In anxiety disorders, a failure to employ the top-down control mechanisms might allow the initial bottom-up responses to disturb regular functioning. On the other hand, there might be an initial exaggerated bottom-up response that cannot be controlled by normal functioning top-down mechanisms.


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Figure 1. Cytoarchitecture of amygdalar structures in a coronal section of a human postmortem brain. Encircled by a blue line is the lateral nucleus (LA). The basolat-eral nucleus (BL), and the basomedial nucleus (BM) are marked with a red line. Other nucleus abbreviations: BV, basoventral; VCo, ventral cortical; Me, medial; Ce, central. Figure adapted with permission from Amunts et al. (2005).

Receiving projections from other amygdalar subnuclei (i.e., lateral and inter-calated cell nuclei) the central nucleus is known as the major amygdala out-put region. Using its widespread subcortical efferent projections to the hypo-thalamus and brainstem, the central nucleus coordinates autonomic activity and regulates emotional and physiological responses. The superficial subdi-vision, is believed to be involved in affective processing (Ball et al., 2007; LeDoux, 2007; Pessoa, 2010).

Neurons in the central nucleus of the amygdala can be inhibited by the GABAergic intercalated cells (Royer et al, 1999). These cells are triggered by inputs from the basolateral nucleus. The importance of the basolateral-central projections has been recently emphasized. Findings from an optoge-netic exploration in mice have shown that this microcircuitry is critical for acute anxiolytic responses (Tye et al., 2011). Moreover, this microcircuitry seems to be well located to allow influences from top-down cortical control regions known to provide dense innervations to the basolateral amygdala. Hence, input coming from the medial PFC to the basolateral subnucleus inhibits the amygdala output, by regulating basolateral inputs to the central amygdala (Kim et al., 2011). Accordingly, it has been shown that electric stimulation of rats PFC resulted in inhibition of conditioned responses (Mi-lad & Quirk, 2002). Therefore, extinction might be achieved by top-down regulatory input from the medial PFC, by influencing the basolateral-central amygdalar circuitry. The importance of the crosstalk between the amygdalaand specific regions of the PFC such as OFC, anterior cingulate cortex (ACC) and medial PFC in emotional regulation is corroborated by anatomi-cal connectivity and human neuroimaging research. This is further discussed below.


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Neurofunctional involvement in SAD The amygdala has been portrayed as a “continuous vigilance system, one that is preferentially invoked in ambiguous situations of biological rele-vance” (Whalen, 1998). The notion that this brain area is evoked by biologi-cal significant stimuli is in conformity with the neuroimaging literature showing amygdalar engagement during biological motion (Bonda, Petrides, Ostry, & Evans, 1996) and when stimuli are social relevant (Adolphs, 2010). An evolutionary model, providing a rational for the involvement of the amygdala in the pathophysiology of SAD is based on the engagement of this limbic structure in appraisal of human facial expressions, and on findings from animal lesion studies highlighting the amygdalar protective function to prepare the organism to avoid danger (Amaral, 2002). Electric stimulation of the amygdala can evoke fearful and defensive behaviors (Davis & Whalen, 2001), anxiety, and social withdrawal (Lanteaume et al., 2007).

Social situations, considered by healthy individuals as neutral or mildly aversive, are known to cause intense anxiety and distress in individuals with SAD (Stein & Stein, 2008). Facial expressions convey powerful emotional information (Ekman, 2003) especially to this group of patients. Birbaumer et al. (1998) carried out the first study examining amygdala reactivity to facial emotion processing probe in SAD patients. The study showed a bilateral amygdalar involvement in the processing of neutral faces in SAD. These findings were corroborated and extended by other studies reporting exagger-ated amygdala responsivity in SAD while viewing neutral and negative faces (Phan et al., 2006a; Stein, Goldin, Sareen, Zorrilla, & Brown, 2002).

Because public speaking is the most prevalent fear (Furmark et al., 1999), a task involving this type of performance provides additional ecologically valid information. During the performance and anticipation of public speak-ing, amygdala hyper responsivity has also been observed (Lorberbaum et al., 2004; Tillfors et al., 2001; Tillfors, Furmark, Marteinsdottir, & Fredrikson 2002). Additionally, amygdala responsivity in SAD has been positively as-sociated with self-reported fear, illness severity and state-trait anxiety scores (Blair et al., 2008; Guyer et al., 2008; Tillfors et al., 2001).

Treatment studies also support the importance of this region in SAD. A decrease in amygdala activity during public speaking after pharmacological or psychological successful treatments has been reported (Furmark et al., 2002, 2005). Clearly this conjunct of sub-nuclei has an important role in the pathophysiology of SAD. Nevertheless, some amygdalar counter-intuitive findings (Britton, Phan, Taylor, Fig, & Liberzon, 2005; Kilts et al., 2006; Phan et al., 2006b; Straube, Glauer, Dilger, Mentzel, & Miltner, 2006) have also been reported. These inconsistencies might be due to task-related fac-tors, genetic influences or even the fact that this region is usually evaluated as a homogeneous region. In fact, although animal findings have long sug-gested that the amygdala is composed of distinct functional and anatomical


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subnuclei, most human neuroimaging studies, including anxiety neuroimag-ing research, commonly analyze the amygdala as a whole.

Hence, amygdala reactivity seems to be a valuable biomarker with signif-icant clinical applications. However, the role of the amygdala in SAD, as well as the underlying mechanisms of this disorder, might be better under-stood if we consider the amygdala as a part of a wide and complex dysfunc-tional circuit involving for example PFC regions.

1.6.2 Emotion regulation - Prefrontal cortex

Anatomical organization Located in the anterior section of the frontal lobe, in front of the motor and premotor areas, the PFC is a vast and functionally heterogeneous region that has been consistently implicated in several complex cognitive and emotional functions. It is known to regulate and control the output of emotion pro-cessing regions like the amygdala (Ghashghaei, Hilgetag, & Barbas, 2007). Broadly, based on cytoarchitecture and anatomical connectivity, the PFC can be differentiated in six distinct anatomical and functional regions (Ray & Zald, 2012). The lateral surface comprises the dorsolateral prefrontal cortex (dlPFC), vlPFC, frontopolar and the OFC. The medial surface can be por-tioned into ventromedial prefrontal cortex (vmPFC) and dorsomedial pre-frontal cortex (dmPFC), the vmPFC including the subgenual ACC and medi-al OFC, and the dmPFC the supragenual ACC – see Figure 2.

Figure 2. Illustration depicting the anatomical organization of the prefrontal cortex (PFC) in six distinct subregions. Lateral surface: dorsolateral prefrontal cortex (dlPFC), ventrolateral prefrontal cortex (vlPFC), frontopolar (FP) and the orbito-frontal cortex (OFC). Medial surface: dorsomedial prefrontal cortex (dmPFC), ven-tromedial prefrontal cortex (vmPFC), and orbitofrontal cortex (OFC). Figure adapted with permission from Gilbert & Burgess (2008).

Connectivity Not all PFC regions implicated in emotion regulation have direct projections to emotion-processing areas like the amygdala. This should be considered


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when making inferences about mechanistic functions. If there is no anatomi-cal support for the functional findings, it is likely that the communication is mediated by other regions. The majority of amygdala’s efferent projections within the PFC are sent to the OFC, medial PFC, and ACC, while projec-tions to the dlPFC are rather weak. Moreover, afferent fibers from the dlPFC to the amygdala are also weak, suggesting that the communication between dlPFC and the amygdala is likely to be indirect. The vlPFC is the only lateral PFC region with a moderate direct input to the amygdala. The amygdala sends the majority of its PFC efferent projections to the posterior OFC, whereas most of the afferent fibers to the amygdala originate from the poste-rior subgenual cingulate and dorsal cingulate (Ray & Zald, 2012). Most of the amygdalar-prefrontal connections are concentrated to the basolateral amygdala (BLA; Kim et al., 2011).

Regarding the connectivity within the PFC, the majority of the communi-cations are between neighbouring areas. However, several subregions of the OFC are reported to have direct communications with the dlPFC. Moreover, due to the heavier and denser projections of the subgenual and dorsal cingu-late to the amygdala, complemented by rich patterns of input coming from the PFC, the anterior cingulate constitutes a good candidate region through which distinct PFC regions influence the amygdala. Hence, the ACC is thought to constitute an important interface between emotion and cognition (Ray & Zald, 2012).

Neuroanatomical involvement in emotion regulation To identify a common functional architecture underlying emotion regulation, neuroimaging studies of cognitive reappraisal, fear extinction, and placebo have been contrasted. Interestingly, the vmPFC was the only mutual cortical region observed (Diekhof, Geier, Falkai, & Gruber, 2011). The importance of this and other cortical regions (i.e., the posterior OFC, and the vlPFC) in emotion regulation was supported by reported negative associations with the amygdala (Ray & Zald, 2012).

In an attempt to elucidate the neurocircuitry involved in emotion regula-tion, Phillips, Ladouceur, and Drevets (2008) proposed a circuit model com-prising the dlPFC, vlPFC, OFC, dmPFC, and ACC, and differentiated be-tween voluntary and automatic types of emotion regulation. The OFC, sub-genual, and rostral ACC are involved in more automatic processes, whereas the dlPFC and vlPFC seem responsible for more voluntary regulation mech-anisms. Regions involved in automatic regulation are thought to be the pri-mary route through which dlPFC and vlPFC exert their influence over the amygdala.

Hence, when limbic regions like the amygdala detect personally relevant salient stimuli, a signal encoding potential threat is sent to the rACC. This region monitors the emotional salient stimuli and triggers cognitive regulato-ry processes in other cortical regions, such as dlPFC. The dlPFC in turn,


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selects, implements, and maintains the regulatory strategies (Goldin et al., 2009). Notably, feedback mechanisms from the prefrontal cortical regions to limbic regions are thought to modulate the course of the emotional response.

Cognitive expectancies of therapeutic outcomes are known to regulate beneficial outcomes. As previously described, anticipatory cortical activity in emotion regulatory brain regions such as the ACC, OFC, dlPFC, and vlPFC has been implicated in expectancy-induced placebo effects (e.g, Wa-ger et al., 2004). Moreover, it has recently been suggested that prefrontal anticipatory activity, particularly in the dlPFC appears to be crucial in trig-gering the downstream therapeutic analgesic responses (Krummenacher et al., 2010). The rACC and OFC have also been consistently implicated in the placebo response. Due to the neurofunctional similarities with reappraisal, it has been suggested that placebo responses might be mediated by active maintenance of beliefs about the treatment changing the way stimulus are evaluated (Wager et al., 2004).

Emotion regulation in SAD The ability to regulate or control emotions is as vital as the ability to respond emotionally (Ochner & Gross, 2005). Failure to properly regulate emotional and fearful responses has been linked to many forms of psychopathology, including anxiety disorders (Cisler & Olatunji, 2012). Neuroimaging evi-dence implicates altered medial PFC activity in anxiety (Kim et al., 2011). Regarding SAD, a diffusion tensor imaging (DTI) study showed a compro-mised structural integrity of the uncinate fasciculus9 which is suggested to underlie the altered amygdala-mPFC circuitry, possibly resulting in dysfunc-tional social threat processing (Phan et al., 2009). Accordingly, Goldin and colleagues (2009) demonstrated that SAD patients fail to recruit PFC subre-gions, during cognitive regulation of emotional reactivity induced by social threat, supporting a disruption in the PFC circuitry in SAD. Abnormalities observed during resting state functional connectivity between the frontal cortex and the amygdala have also been reported (Hahn et al., 2011; Liao et al., 2010). Together, these studies provide empirical evidence emphasizing the cognitive models suggesting that SAD patients fail to recruit proper cog-nitive regulatory neural circuits.

There are, however, some problems with this crude prefrontal inhibitory neurobiological model. Studies reporting an association between high levels of anxiety with decreased vmPFC and increased dmPFC, suggest distinct roles for ventral (regulation) and dorsal (expression) medial PFC (Etkin, Egner, & Kalisch, 2011). Moreover, whereas inhibition of the amygdala by vmPFC is thought to be a crucial mechanism affected in anxiety and mood

9A major white matter fiber tract known to connect the amygdala and the OFC.


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disorders other lines of research suggest that some areas of the vmPFC might promote, rather than inhibit, the experience of negative affect (Myers-Schulz & Koenigs, 2011). Moreover, cognitive representations of social anxiety such as negative self-evaluation and rumination have been associated with hyperactivity within prefrontal cortical regions. Studies have reported that OFC, and ACC activity in depressed patients is associated with ruminative thoughts, and a loss of communication between prefrontal and subcortical structures reduces symptoms of depression and anxiety. Accordingly, de-creased responsivity in these areas has also been reported to reflect a reduc-tion of ruminative and self-focused thoughts (Cooney, Joormann, Eugène, Dennis, & Gotlib, 2010), which is a major vulnerability factor for SAD. During emotional challenges several studies have also reported that the neu-ral activity in the amygdala and PFC regions change in the same direction (Furmark, 2009) which becomes difficult to integrate with the inhibitory neurobiological model. These neural processes might result in top-down excitation rather than inhibition.

As proposed by Myers-Schulz and Koenigs (2011) a closer look at differ-ent anatomical subregions might help clarifying the contradictory findings. In fact, these authors have gathered evidence suggesting that whereas the posterior vmPFC is associated with negative affect, the perigenual or anteri-or vmPFC is related to positive affect. Hence, an effort to understand these inconsistent findings and the underlying specific (i.e., subregional) neurocir-cuitry behind emotion regulation in anxiety not only helps in elucidating the nature of the disorders, but also suggests specific avenues for the develop-ment of new efficient cognitive and pharmacological treatments.

1.7 Serotonin and anxiety Understanding the neurochemistry of anxiety is fundamental for the devel-opment of new anxiolytic treatments. Numerous neurotransmitters such as glutamate, gamma-aminobutyric acid (GABA), and the monoamines nora-drenaline, dopamine and serotonin are thought to have important roles in the pathophysiology of SAD (Durant, Christmas, & Nutt, 2009). Although all these neurotransmitters might be equally important for anxiety, due to the known therapeutic efficacy of SSRIs, serotonin is the most relevant for this thesis.

Anatomical evidence suggests that serotonergic systems are topograph-ically organized within the brainstem raphe nuclei, with its neurons distrib-uted through different regions of the brainstem (i.e., median and dorsal raphe nuclei) that project to distinct functional systems (Lowry, Johnson, Hay-Schmidt, Mikkelsen, & Shekhar, 2005). Serotonin is synthesized from the dietary amino acid tryptophan and it is known to regulate several functions

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including appetite, sleep and impulse control (Bloom, 1995). The variety of these functions might be attributed to the extensive distribution of sero-tonergic projections and multiple receptors. With regard to projections, there are two major serotonergic pathways. One that descends from the caudal raphe into the spinal cord and another that ascends from the medial and dor-sal raphe to widespread areas of the central nervous system.

Based on cytoarchitectonic criteria, studies suggest a heterogeneous dis-tribution of dorsal raphe neurons projecting to distinct anxiety related areas such as the mPFC, hippocampus, NAc, BLA and putamen (Lowry et al., 2005). Distinct serotonergic neurons are proposed to influence the neural mechanisms underlying initiation, maintenance or cessation of anxiety relat-ed behaviors and anxiety states in a distinct way (Lowry et al., 2005). Allelic variation in serotonin related genes has also been showed to modulate amyg-dala responsivity in SAD further supporting the role of serotonin in the path-ophysiology of this disorder (Furmark et al., 2004).

1.7.1 Imaging genetics The completion of the human genome sequence provides unique opportuni-ties to advance our understanding on genetic individual differences regarding the vulnerability to develop and the capacity to resolve neuropsychiatric disorders like SAD. Moreover, because most of the genes are expressed in the brain, combining neuroimaging with molecular genetic techniques per-mits the investigation of how the functional polymorphisms affect brain function (Hariri, Drabant, & Weinberger, 2006).

Considerable individual variations in trait negative affect are important predictors of vulnerability for several disorders including SAD (Domschke & Dannlowski, 2009). The contribution of genetic factors to the pathogene-sis of SAD has been reported to be as high as 51% (Hettema et al., 2001), and is thought to be composed of complex genetic mechanisms under the influence of multiple risk genes and gene-environment interactions (Dom-schke & Dannlowski, 2009). So, the identification of gene variants, their impact on clinical outcomes and the neural intermediate phenotypes related to SAD are therapeutically relevant.

5-HTTLPR SSRIs block the serotonin transporter that is expressed by the serotonin transporter gene (SLC6A4). In this gene, the majority of the attention has been drawn to a variable number of tandem repeats polymorphism in the promotor region - serotonin transporter gene linked polymorphism region (5-HTTLPR). This polymorphism has two variants, a short allele (s) comprising 14 copies of a 20-23 base pair repeat unit, and a long form (l) comprising 16 copies. In vivo evidence suggests that the long form is functionally more active than the short one (Lesch et al., 1996). In a pioneer study an associa-


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tion between the s allele and enhanced levels of anxiety traits (neuroticism) was reported (Lesch et al., 1996). Since then, the s allele has been linked with anxiety-related personality traits, heightened fear conditionability, and life stress induced affective disorder (Serretti, Calati, Mandelli, & De Ron-chi, 2006).

The amygdala is densely innervated by serotonergic neurons and seroto-nin receptors are abundantly found throughout its subnuclei (Bauman & Amaral, 2005). Hence, the activity of this region is likely to be sensitive to changes in serotonergic neurotransmission. The resulting variability in amygdala responsivity most likely affects individual differences in mood, temperament and anxiety disorders. The first imaging genetic study, showed that s allele carriers exhibit increased amygdala reactivity in response to fearful stimuli when compared to ll homozygotes (Hariri et al., 2002), which is in line with Garpenstrand, Pissiota, Ekblom, Oreland, and Fredrikson’s (2001) study showing an increased conditionability in carriers of the s allele. Hariri and colleagues’ findings (2002) have been replicated in healthy sub-jects (Domschke & Dannlowski, 2009), as well as depressed patients (Dannlowski et al., 2007), and the impact of this influence was confirmed recently by a meta-analysis (Munafò, Brown, & Hariri, 2008). With regard to anxiety, SAD patients carrying the s allele exhibited elevated right amyg-dala activity in comparison to ll homozygotes, during anxiety provocation, and this was accompanied by elevated trait and state anxiety (Furmark et al., 2004).

As reported above, several studies have explored how different brain re-gions (e.g., amygdala and mPFC) interact to regulate emotion behavior. In line with these studies, imaging genetic studies have also started to explore the effects that variations in the 5-HTTLPR gene have on this functional connectivity. Uncoupling of the feedback circuit between perigenual cingu-late and amygdala in response to fearful faces, was shown to account for about 30% of the variance scores in harm avoidance (Pezawas et al., 2005). Moreover, this uncoupling was related to s carriers suggesting that this ge-netic modulation might contribute to elevated harm avoidance that in turn might increase the risk for anxiety disorders (Pezawas et al., 2005). With regard to SSRI treatment, the short allele has also been associated with a poor therapeutic response in SAD patients (Stein, Seedat, & Gelernet, 2006a) and panic disorder patients (Perna, Favaron, Di Bella, Bussi, & Bel-lodi, 2005).

TPH-2 Allelic variations in other genotypes such as the tryptophan hydroxylase-2 (TPH2) G-703T (rs4570625), a polymorphism located in the upstream regu-latory region of TPH2, might also underlie inter-individual variability in amygdala, influencing behavior. TPH2 encodes the enzyme that catalyzes the rate limiting step of serotonin synthesis in the brain. The G-703T poly-

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morphism has been linked to affective spectrum disorders. Developmental studies have suggested an association of the T allele with temperamental emotion regulation via its effect on attention disengagement. These findings implicate the TPH2 gene in variations in early cognitive abilities as potential developmental precursors of individual differences in emotion regulation and risk for the development of affective disorders (Leppänen, Peltola, Puura, Mäntymaa, Mononem, & Lehtimäki, 2011). Accordingly, amygdala hyperresponsivity to emotional facial stimuli has been reported in T carriers, in comparison to G homozygotes in healthy adults (Brown et al., 2005; Can-li, Congdon, Gutknecht, Constable, & Lesch, 2005) as well as in SAD pa-tients (Furmark et al., 2009), supporting the relevance of this polymorphism in psychopathology. Furthermore, healthy T allele carriers had smaller vol-umes in the amygdala and hippocampus and higher reward dependence (In-oue et al., 2010).

Overall, imaging genetics not only enable the identification of individuals at risk, but also enlighten the biological pathways for the development of more efficient and individualized treatments.

1.7.2 SSRI treatment The pharmacotherapy of anxiety disorders was dominated by benzodiaze-pines that have proven efficacious in several anxiety disorders during the second half of the past century. Even though benzodiazepines still belong to the current therapeutic repertoire, in short term treatment situations, SSRIs, with improved tolerability and safety, are the first-line pharmacotherapy in anxiety disorders (Blanco, Braqdon, Schneier, & Liebowitz, 2012). Consid-erable evidence concerning the efficacy of pharmacological treatments of SAD exists for several SSRIs (Hoffman & Mathew, 2008). However, the role of serotonin and the mechanisms by which neurochemical and neural changes induced by SSRIs result in a therapeutic effect are still incompletely understood.

SSRIs inhibit the function of the serotonin transporter, which is responsi-ble for the reuptake of serotonin, preventing it from being transported back into the presynaptic neuron. Inhibition of the serotonin transporter leads to an accumulation of serotonin in the extracellular space resulting in increases in the magnitude and duration of serotonergic activity on pre and postsynap-tic serotonergic receptors. Although SSRIs inhibit the reuptake of serotonin within hours of administration, conventional knowledge suggests that thera-peutic effects of SSRIs take at least a couple of weeks before becoming evi-dent. Animal studies, targeting the amygdala, have shown that acute admin-istration of SSRIs result in amygdala hyperresponsivity and associated be-havioral responses (Burghardt, Sullivan, Mcewen, B, Gorman, & LeDoux, 2004; Foster et al., 2006). Human research has supported these findings by


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showing that acute citalopram administration potentiates the reactivity of the human amygdala to salient stimuli (Bigos et al., 2008).

Following chronic SSRI administration, studies have shown an attenuated amygdala response to stressful stimuli or situations in healthy volunteers (Harmer, Mackay, Reid, Cowen, & Goodwin, 2006), depressed (Fu et al., 2004; Sheline et al., 2001) and in SAD (Furmark et al., 2002, 2005) patients. In these studies other regions such as hippocampus, and fronto-parietal areas were also attenuated by SSRI administration. This might suggest that bot-tom-up processes underlie SSRI treatment.

So far, it seems that acute and chronic SSRI administration result in oppo-site therapeutic effects. This supports conventional knowledge foreseeing an early exacerbation of anxiety followed by an anxiolytic effect. Contrary to this, however, clinical practice suggests that some patients improve earlier. Nevertheless, early improvement has been traditionally attributed to a place-bo effect rather than a pharmacological effect (Quitkin et al., 1987). A recent meta-analytic study suggested, however, that SSRIs might indeed have a much earlier therapeutic effect than originally thought (Taylor, Freemantle, Geddes, & Bhagwagar, 2009).

An early onset of therapeutic effects is supported by studies showing that acute SSRI administration is accompanied by decreased amygdala respon-sivity (Anderson et al., 2007; Murphy, Norbury, Sullivan, Cowen, & Harm-er, 2009), and reduced cognitive bias (Harmer et al., 2003). SSRI treatment has also been associated with an increased negative coupling between coti-co-limbic structures (Chen et al., 2008). Additionally, resting state connec-tivity findings also show that SSRIs reshape cortical control at an early stage of treatment, making the PFC a plausible target for SSRI (McCabe & Mishor, 2011). Hence, this recent line of research suggests that SSRIs might work by modifying specific neural dysfunctions related to negative cognitive bias and that these changes might occur early in treatment (Di Simplicio, Norbury, & Harmer, 2012).

Overall, there is still a long way to go until we reach a reliable under-standing of the complex mechanisms behind SSRI treatments. However, also noteworthy are the findings coming from independent meta-analytic studies questioning the pharmacological efficacy of these drugs and suggesting that their beneficial effects can be largely accounted for by placebo (Fournier et al., 2010; Khan, Leventhal, Khan, & Brown, 2002; Kirsch et al., 2008). In fact, Fournier and colleagues (2010) recently reported that the magnitude of these pharmacological treatments might be minimal or even nonexistent, when compared to placebo in patients with mild or moderate symptoms. Pharmacological drugs seem, however, to be substantially superior to place-bo in more severe cases (Fournier et al., 2010). Therefore, both from the individual and societal perspective, it is imperative to put some effort into the investigation of the neurobiological mechanisms underlying successful

SOCIAL ANXIETY – CLINICAL PLACEBO MODEL

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SSRI and placebo treatments, aiming at advancing treatment research, and ultimately trying to tailor treatments to the specific needs of the patients.

1.8 Social anxiety - Clinical placebo model It has been argued that if there is consistent evidence from clinical trials showing that placebos produce clinical improvement, then there might be a legitimate place in medicine for adapting strategies and interventions to promote the placebo effect (Miller & Colloca, 2009). In fact, even though most of the research supporting placebo’s beneficial effects comes from experimental studies (Vase et al., 2002), meta-analyses comparing placebo outcomes with no-treatment control groups showed significant effects not only in the analgesia domain but also with regard to anxiety (Hróbjartsson & Gotzsche, 2004). However, even though anxiety, just like pain, passed the no-treatment control group test, anxiety has been understudied in this field. There is, nevertheless, data from RCTs showing a moderately large placebo response (mean d = 0.46) in SAD trials (Oosterbaan et al., 2001).

Also, as stated above, when comparing antidepressive/anxiolytic medica-tion with placebos in RCTs, meta-analytic results suggested that the small percentage observed in the active medication group might actually reflect an active placebo response (Kirsch, Moore, Scoboria, & Nicholls, 2002; Kirsch, & Sapirstein, 1998). These findings have been supported by studies suggest-ing that once the methodological problems with clinical trials are taken into account (e.g., blindness), these pharmacological treatments are either inef-fective or have such small effects that they become clinically irrelevant (An-drews, 2001; Moncrieff, 2002). Accordingly, evidence for placebo-induced anxiety relief has also been reported in anxious post-operative patients who after an open administration of diazepam reported significantly lower levels of anxiety in comparison to the ineffective hidden administration of diaze-pam (Colloca et al., 2004). Together these studies advocate that placebos have clinically significant effects in affective disorders, supporting the ne-cessity of investigating the neurobiological mechanisms underlying this ben-eficial response.


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2. Aims

The present thesis is based on four studies aimed at expanding our knowledge on the neurobiological processes underlying placebo responses.

i. The first paper reviews functional neuroimaging research of the pla-cebo phenomenon across studies and conditions to evaluate the ex-istence of common or segregated neural patterns.

This review is followed by three empirical PET studies measuring sustained anxiolytic placebo responses, during a stressful task, in a clinical SAD popu-lation.

ii. The first empirical paper investigated how genetic polymorphisms

influence neural activity and the associated placebo response pheno-type to determine the neurobiological predictors of successful place-bo-anxiolysis.

The last two studies included comparisons with the pharmacological treat-ment of choice for anxiety disorders i.e., SSRIs.

iii. The second empirical paper examined both neural and behavioral

commonalities and differences in responders and nonresponders to placebo and SSRIs.

iv. Finally, the fourth study evaluated differential and shared amygdala-PFC connectivity patterns, between placebo and SSRI treatments to further clarify whether successful psychological and pharmacologi-cal treatments operate via similar or different neural pathways.

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3. Methods

3.1 Clinical trials The empirical papers of the current thesis are based on data extracted from three randomized double-blind clinical PET trials evaluating changes in rCBF following treatment with SSRIs, Neurokinin 1 (NK1) antagonists, a combination of NK1 antagonists with SSRIs and placebo (N=144). There were three treatment arms in the first two trials and six arms in the third. The first trial lasted 6 weeks, whereas the last two trials lasted 8 weeks. In this thesis data regarding NK1 antagonists and the combination of NK1 antago-nists with SSRIs were not evaluated (i.e., two arms of the first two trials and four arms of the last trial were excluded), only the patients treated either with SSRIs or placebo (n=72) were considered for the analyses presented in this thesis – see Figure 3. The first empirical study is based on data from 25 SAD patients treated with placebo, while the last two empirical studies are based on data from 72 SAD patients including the placebo treated patients (n=37), and patients treated with SSRIs (n=35). All trials were performed at the PET center in Uppsala, with the collaboration of Quintiles AB Uppsala (Sweden), and GlaxoSmithKline Verona (Italy).

All participants gave their informed consent after the nature and conse-quences of the study had been explained. The studies were pre-approved by the Swedish Medical Products Agency as well as the Uppsala University Medical Faculty Ethical Review Board and the Uppsala University Isotope Committee.

3.2 Participants and recruitment All participants were recruited through newspaper advertising. Following a short telephone interview, participants were asked to fill out a battery of social anxiety questionnaires and return them by email. The psychiatric sta-tus was evaluated by a clinical psychologist using the anxiety disorder sec-tion of the SCID for the diagnostic and statistical manual of mental disorders (DSM-IV; American Psychiatric Association, 2004). Additionally, the MINI-interview (Sheehan et al., 1998) was administered by a psychiatrist in order to exclude severe psychiatric disorders other than comorbid anxiety conditions. Medical examinations were also performed. SAD was the main


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diagnosis for all patients included in this thesis (n=72) and a marked public speaking anxiety was declared by all. Forty-nine patients (68%) were diag-nosed with the generalized subtype and 14 (19%) qualified for a comorbid anxiety disorder: specific phobia (n=9), generalized anxiety disorder (GAD; n=1), posttraumatic stress disorder (n=1), panic disorder (n=1), and specific phobia together with GAD (n=2). The main criteria for exclusion were: (1) treatment of social anxiety in the past six months; (2) current serious or dom-inant psychiatric disorder other than SAD (e.g. psychosis, major depressive or bipolar disorder), (3) chronic use of prescribed medication, (4) abuse of alcohol/narcotics, (5) pregnancy, (6) menopause, (7) left handedness, (8) previous PET-examination, and (9) any somatic or neurologic disorder that could be expected to influence the outcome of the study.

Figure 3. Flow diagram of subjects’ eligibility from screening to evaluation. Study II included patients randomized to placebo (responders n=10 and nonrespond-ers n= 15) from trials II and III. Study III and IV included both SSRI and placebo subgroups from trials I, II and III (SSRI responders n= 20, SSRI nonresponders n= 15; placebo responders n=11, placebo nonresponders n=26).

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3.3 Treatment procedure GlaxoSmithKline provided the daily doses of the drug and placebo for the three trials. Each trial had a fixed SSRI dosing schedule – see Figure 3. All participants received the first dose immediately after the first PET examina-tion and the last dose was given 2-4 hours before the final PET assessment. No other forms of treatment were used during the study period. Assessments of compliance and side effects were performed biweekly, when patients vis-ited the clinic to receive new supplies of medication. During those occasions, vital signs were checked, laboratory safety tests were performed and anxiety scales were administered. After completion of the study, patients were of-fered the possibility to obtain additional psychiatric consultation and phar-macotherapy.

3.4 Experimental public speaking task Although neuroimaging studies investigating SAD during the processing of facial expressions of emotions have yielded important results (Freitas-Ferrari et al., 2010), their ecological validity can be questioned. Considering that patients with SAD fear scrutiny, performance failure and humiliation in a variety of social situations and that public speaking is the most prevalent social fear (Furmark et al., 1999), symptom provocation studies in which the patient has to undergo a performance situation, such as giving a speech in front of an observing audience, resemble a more naturalistic situation and are therefore more likely to reveal the neural foundations of the symptomatic emotional experience. A large number of worldwide studies have validated this anxiolytic task (e.g., Davidson, Marshall, Tomarken, & Henriques, 2000; Lorberbaum et al., 2004).

Hence, in order to evaluate the neural underpinnings of treatment in social anxiety, in the three RCTs, patients were scanned before and after treatment during an anxiogenic public speaking task. While lying in the scanner, and directly after tracer injection patients gave a 2½-minute speech about a vaca-tion or travel experience10 in the presence of a silently observing audience of 6-8 persons – see Figure 4. During the speech, patients were instructed to observe the audience, and to increase observational anxiety, a member of the audience was recording them with a videocamera. Immediately after the speech, self-reported behavioral measures of anxiety and fear were obtained.

10 Patients were instructed to prepare the speech about 20 minutes before the initial emission scan. The speech topic was different after treatment.


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Figure 4. The anxiety-provoking public speaking task from the patients’ perspective, while lying in the scanner.

3.5 Clinical behavioral measurements

3.5.1 Clinician-rated anxiety measures To evaluate treatment effectiveness, the Clinical Global Impression (CGI) scale was administered biweekly by a psychiatrist and therapeutic effects were indexed by the Global Improvement subscale (CGI-I). The subscale ranges from 1 (very much improved), to 7 (very much worse). On the day of the final PET-assessment patients having a score of 1 or 2 (much improved) on the CGI-I were classified as responders, whereas those having scores of 3(minimally improved) or higher (4=no change, 5-7=worse) were considered non-responders.

Complementary to CGI, Liebowitz Social Anxiety Scale (LSAS) (Lie-bowitz, 1987) was also administered by a psychiatrist but only at screening and on the final PET assessment. LSAS is used for the purpose of measuring the degree of avoidance and fear in social situations (24 situations) on a Lik-ert scale ranging from 0 to 3.

3.5.2 Self-report anxiety measures Commonly used in anxiety research, the state portion of the Spielberger State-Trait Anxiety Inventory (STAI-S) (Spielberger, Gorsuch, & Lushene, 1970) is considered the gold standard in the field. STAI-S is found to be a sensitive indicator of changes in transitory anxiety and consists of 20 items. The scale ranges from 20 to 80, and scores between 20 and 39 indicate low anxiety, 40 to 59 indicate moderate anxiety and scores between 60 and 80

METHODS

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reflect high anxiety. STAI-S was administered after the public speaking tasks.

Subjective ratings of fear were also administered after the public speaking challenges. Here, subjects were asked to rate on a visual analogue scale be-tween 0 (minimum) and 100 (maximum) how much fear they felt during the sessions (Furmark et al., 2002).

At screening and on the day of the final PET assessment patients com-pleted two social anxiety questionnaires: The Social Phobia Scale (SPS), and Social Interaction Anxiety Scale (SIAS), (both range 0-80, min-max).

3.6 Merging the groups In order to increase the statistical power and to improve the generalizability of the findings, SSRI and placebo data was merged from the three trials. This was supported by statistical analysis showing no significant differences (p’s>10) with regard to demographic or clinical variables before treatment within SSRI and placebo arms across the three trials.

3.7 Genotyping Because in study II the aim was to examine the association between seroto-nin-related genetic polymorphisms, stressful neural activity, and placebo resultant anxiety relief, genotyping was performed. Blood samples, available for 23 of the 24 subjects included in Study II, were collected in Uppsala and analyzed in Gothenburg – details regarding genotyping are provided in the genotyping section of the corresponding study. To avoid mass comparisons, analyses were focused on two serotonin-related polymorphisms known for their modulatory influence over the amygdala. Both the 5-HTTLPR and the TPH2-gene were dichotomized. In the 5-HTTLPR, comparisons were made between patients carrying at least 1 short allele (s) with those homozygous for the long allele (ll) (Hariri et al., 2002). Regarding the TPH2 gene, T al-lele carriers were compared with G allele homozygotes (Brown et al., 2005). None of the genotype distributions differ significantly from the Hardy-Weinberg equilibrium.

3.8 Positron Emission Tomography Developed in the mid-1970s at the Mallinckrodt Institute of Radiology at Washington University, the nuclear imaging PET technique is a valuable research tool to study the living human brain. In comparison to imaging mo-dalities such as CT or MR, PET offers the possibility to image metabolism,


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pharmacokinetics, biochemical and physiologic functions. To study these specific biological processes in vivo a number of organic molecules such as water or more complex molecules such as raclopride11 can be labeled with positron emitting radionuclides12. Due to a long half-life, naturally occurring radionuclides cannot be used. Hence, the majority of the radionuclides in-corporated into biological active molecules i.e., radiotracers, used in clinical practice and research are short-lived and produced by particle accelerators or cyclotrons located in the vicinities of the PET imaging facilities (Kimura, 2002).

Among the most commonly used radionuclides are 11C, 18F and 15O with approximately 20, 110 and 2 half-life minutes, respectively. Whereas the first two offer the possibility to investigate radiosynthesis with more than one step and detection of physiological mechanisms with slower pharmaco-kinetics, 15O, being an extreme short-lived radionuclide, is particularly suited for blood flow tracers. The scanning times of blood flow studies are no long-er than 2.5 minutes. A major advantage of these short-lived isotopes is the low radiation dose that allows the repetition of the studies within a short period.

Common PET applications in neuroscience are related to the investigation of neurotransmitters and their receptors and also the brain “activation” stud-ies, where changes in rCBF are measured in order to infer brain function. The three empirical studies presented in this thesis used a PET scanner to image rCBF. Regional CBF can be used as an indirect measure of neural activity. The relation between brain blood flow and brain function or neural activity was first proposed by the physiologist Angelo Mosso at the end of the nineteenth century after observing that alterations in brain blood flow, seemingly independent from changes in heart rate or blood pressure, were related to mental activity. The pioneer work developed by Mosso and others set the stage for measurements of brain blood flow and metabolism with PET in modern neuroscience. Currently, a tight relationship between neural responses and the brain hemodynamics is widely accepted (Toga & Mazziot-ta., 2000). It is known that rCBF is regulated to meet the brain’s metabolic demands. Briefly, increases in neuronal activity will increase energy requirements, i.e., the metabolic demand for glucose and oxygen, that are accompanied by a local increase in CBF.

In PET, the positron emitting radioactive isotope 15O-water, a freely dif-fusible inert tracer, is injected intravenously and over the following minute this radiotracer accumulates in the brain proportionally to the local blood flow. Inside the patient, the unstable positively charged radioactive isotope

11Raclopride - D2 dopamine receptor antagonist. 12An atom with an unstable nucleus i.e., radioactive isotope.

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decays and discharges positrons. After travelling just a few millimeters dis-tance (3-5 mm) in the brain tissue the positron encounter its antimatter coun-terpart, the electron, and a reaction producing gamma rays occurs i.e., anni-hilation reaction. In this reaction, these two particles are annihilated by con-version of their mass into energy released in the form of two photons, leav-ing the area of annihilation at exactly 180 degrees from each other. The photons are registered by a ring of detectors positioned around the patient’s head in the PET camera. The PET detectors record a radioactive event only when struck by annihilation photons simultaneously i.e., a valid annihilation event requires a coincidence within 12 nanoseconds between two detectors on opposite sides of the scanner, which enables the position for the collision between the positron and the electron to be determined.

Hence, this nuclear 3D imaging technique is based on the detection of ra-dioactivity i.e., annihilation of the administered compounds. With the help of statistical computer programs tens of thousands of annihilation events can be calculated into a PET-image showing the distribution of the radioactive nu-clides in the patient’s brain. In the case of 15O-water, the greater the blood flow, the more radiation counts.

3.8.1 Scanner specifications In the presented studies, data was sampled with a 3D 32 ring ECAT EXACT HR + scanner (Thermo Fisher Scientific/CTI). This scanner enables the ac-quisition of 63 contiguous planes of data with a distance of 2.46 mm result-ing in a total axial field of view of 15.5 cm. A transmission scan was per-formed, during 10 min, using three retractable Germanium-68 rotating line sources. Subsequently, the water tracer was injected intravenously, ~10 MBq/kg body weight, and data collection (i.e., emission scans during pa-tients speech) started when the bolus reached the brain (50,000 counts/s). Emission scans consisted of three 30s frames. Images were reconstructed with a filter back projection using an 8mm Hanning low pass filter, resulting in a spatial resolution of approximately 5mm in the field of view. Data was corrected for photon attenuation, decay, scattered radiation and random co-incidences. After reconstruction, a summation image of the three frames was made to reach a better statistical reference for realignment and subsequent analyses.

3.8.2 Pre-processing In order to perform statistical analyses and to make inferences about effects of interest, PET images need to go through some spatial preprocessing steps. During realignment, images are aligned with each other to compensate for small motion artifacts and to correct for different positions between pre-


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treatment and posttreatment scans. This motion correction step ensures that scans within the same subject occupy the same space.

Like everything else in the human body, brains differ in size and shape, so to be able to average and to compare several subjects, images need to be normalized. During this step scans are warped to fit the template of a stand-ard brain so they have the same spatial representation. In this thesis the Mon-treal Neurologic Institute (MNI) anatomical template was used.

After spatial normalization, some anatomical inter-subject variability still remains. To account for this variability and to increase the signal to noise ratio, images should also be smoothed. This step consists of convolving the data using a Gaussian kernel, so that each voxel is influenced by its neigh-boring voxels. Smoothing enables the assumption that noise is distributed in a random and independent fashion. In the first empirical study a 12 mm Gausian kernel was used. However, due to the fact that in studies II and III the focus was on amygdala subregions, an 8 mm kernel was used.

3.8.3 Statistical modeling and inference After the pre-processing steps, data is ready to be analyzed. PET data are represented by more than 100,000 voxels (2×2×2 mm) assigned with a posi-tion and a blood flow value. A statistical model is fitted into the data to spec-ify the parameters which are then going to be used to look for the effects of interest. In other words, the observed signal is divided into components ac-counting for the experimental condition and the error term. Once the design matrix is set up, SPM estimates how much every condition contributed to the data and how much error is left after every condition is considered. The es-timation of the model is followed by statistical contrast analyses which are calculated for each voxel in the brain.

The GLM, an equation that expresses the observed response variable in terms of a linear combination of explanatory variables plus an error term, is used to implement these analyses (Friston et al., 1995). Statistical parametric mapping was performed with the Matlab based neuroimaging software SPM2 in the first and second study and SPM8 in the third study (www.fil.ion.ucl.ac.uk/spm). Brain locations are described as x y z coordi-nates in standard Talairach space, obtained by affine transformation of the MNI coordinates. A detailed explanation of each statistical procedure is pro-vided in the method section of each individual study at the end of the thesis.

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4. Summary of studies

4.1 Study I 4.1.1 Background & aim For more than a decade functional neuroimaging techniques have been used to explore the underlying brain mechanisms behind placebo responses (e.g., de la Fuente-Fernández et al., 2001). While enabling a quantifiable measure of brain activity, neuroimaging studies uncover the neurobiology of the pla-cebo phenomenon supporting its clinical significance. Placebo imaging stud-ies have shown that successful placebo responses are not restricted to one type of therapy or condition (e.g., Petrovic et al., 2002; Mayberg et al., 2002). Hence, study I aims at reviewing the available placebo imaging litera-ture to examine the existence of common and or segregated neural patterns across studies and conditions.

4.1.2 Literature search In an effort to provide a comprehensive but comparable compilation of both cortical and subcortical neural findings, only PET and fMRI studies were included. From the 24 selected studies, 11 measured hemodynamic respons-es with PET and fMRI whereas the others have looked into the neurochemis-try. Concerning the hemodynamic studies, they were grouped and discussed based on the conditions investigated i.e., placebo analgesia, placebo acu-puncture analgesia and placebo anxiolysis. With regard to the neurochemical studies, findings were organized and discussed based on the tracers used i.e., 11C-CAR, 11C-RAC and 18FDG. Additionally, to identify common and dis-tinct brain activity patterns across conditions and studies, an integrative ap-proach was used throughout the review.

4.1.3 Results With regard to placebo analgesia, imaging studies support the previous find-ings (Benedetti et al., 1999) suggesting involvement of both opioidergic and non-opioidergic mechanisms (e.g., Kupers et al., 2007; Zubieta et al., 2005). A shared modulatory network, including dlPFC and rACC is implied across placebo studies. Overall, placebo responses seem to be mediated by top-


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down processes, initiated in frontal cortical areas responsible for generating and maintaining cognitive expectancies.

Reported ACC activations and deactivations were plotted and apparent ACC inconsistencies were discussed based on the proposed functional dis-tinctions between the cognitive dorsal and the affective rostral sections of the ACC (Bush, Luu, & Posner, 2000) – see Figure 8 on page 61. With its privi-leged connections with the prefrontal and limbic structures, projections to the spinal cord and dopamine neurons, the ACC might constitute an im-portant site in modulating placebo responses. Taken together imaging studies suggest that while effective placebo treatments involve disorder-specific neural responses, these responses seem to be triggered by a common modu-latory cognitive factor reflecting reward expectancies, that appears to play a crucial role in successful placebo responses regardless the conditions.

4.1.4 Conclusions With this overview it becomes clear that neurofunctional knowledge about placebo stems critically from the experimental field of analgesia with some insights coming from PD, and a couple of exceptions originating from emo-tion regulation and depression.

Even though considerable progress has been achieved in placebo-related neurobiological underpinnings, which might lead to a tentative suggestion of a general placebo triggering mechanism, the neurobiological investigation of placebo effects in clinical disorders is largely lacking. Hence, drawing con-clusions about a common general neural pathway underlying placebo re-sponses with therapeutic validity across conditions becomes challenging and still premature.

In order to advance, we need to expand our neurobiological knowledge, which nowadays comes mostly from experimental studies, to clinical inves-tigations that will allow an evaluation of these responses during larger peri-ods of time (i.e., clinical relevant periods of time). Hence, it is important that progress achieved in experimental studies paves the way for clinically ori-ented placebo research with the aim of translating these experimental find-ings to clinical trials and from these clinical trials to clinical practice.

4.2 Study II 4.2.1 Background & aim Accumulating evidence shows that placebos may yield beneficial responses comparable with pharmacologically-induced responses (Petrovic et al., 2002, Mayberg et al. 2002; see also Study I). In SAD patients, attenuated amygdala activity has been reported after successful pharmacological treatment (Fur-

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mark et al., 2002) and a link between amygdala reactivity and serotonin-related genes has been provided (Furmark et al., 2004).

Although there is evidence suggesting that certain individuals are more prone to respond to placebo than others (Scott et al., 2007) the underlying genetic proneness to develop a successful placebo response has not been examined. Moreover, most placebo imaging literature is focused on experi-mental pain whereas imaging studies investigating clinical sustained placebo responses are largely lacking. Study II used PET to investigate how sero-tonergic polymorphisms influence stress-induced neural activity and the accompanying placebo phenotype during prolonged placebo treatment of SAD.

4.2.2 Results After 8 weeks of placebo treatment, a bilateral amygdalar attenuation was noticed, during the stressful public speaking task, in responders (n=10) as compared to nonresponders (n=15). Attenuated amygdala activity was only observed in subjects that were homozygous for the TPH2 G allele and for the ll variant of the 5HTTLPR. However, only the TPH2 polymorphism emerged as a significant predictor of placebo-induced anxiety relief with a better placebo response in GG homozygous patients – see Figure 5. Path analysis further suggested that the TPH2 genetic effect on placebo anxiolysis was mediated by its influence on amygdala activity.

Figure 5. Image displaying GG allele homozygotes compared to T allele carriers of the tryptophanhydroxylase 2 (TPH2) gene – left and right amygdala attenuation. In the interaction plots, filled circles refer to GG homozygotes, whereas crosses refer to T allele carriers.

4.2.3 Conclusions These results point to a modulatory role for serotonin in placebo anxiolysis. After prolonged placebo SAD treatment, anxiolysis was accompanied by reduced amygdala reactivity only in patients homozygous for the l allele of the 5-HTTLPR and the G allele of the TPH-2 polymorphisms, although only


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the latter could be tied to reduced anxiety behaviorally. This suggests a pathway from the genotype via the brain endophenotype to the behavioral phenotype. For the first time a link between gene polymorphisms, amygdala activity and placebo anxiolysis was shown, implying that placebo responses might not only be context mediated but also under genetic influence.

4.3 Study III 4.3.1 Background & aim High placebo responsivity constitutes an actual challenge when trying to establish the efficacy of new pharmacological treatments in clinical trials (Alphs et al., 2012). In SAD, anxiety relief is accompanied by amygdalar attenuation after sustained placebo treatment, as shown in study II. Likewise, amygdala attenuation has also been reported in SAD patients after SSRI treatment (Furmark et al., 2005). Lower amygdalar responsiveness seems to be an important neuronal change for anxiety alleviation to occur regardless of treatment modalities. However, counterintuitive amygdala findings have also been reported (e.g., Britton, Phan, Taylor, Fig, & Liberzon, 2005; Maslowsky et al., 2010).

While meta-analyses have raised concerns regarding the clinical effec-tiveness of SSRIs over placebo (Kirsch et al., 2008), studies investigating neurofunctional commonalities and differences between placebos and SSRIs are scarce and contradictory (Mayberg et al., 2002; Leuchter et al., 2002). The aim of study III was to examine behavioral and neurofunctional com-monalities and differences between pharmacologically and placebo induced anxiolysis in patients with SAD using PET.

4.3.2 Results Attenuated amygdalar activity (rCBF) was noticed from pre to post treat-ment within all treatment subgroups. Moreover, common amygdala attenua-tions were found specifically in the left BM/BLA and right ventrolateral amygdale (VLA) for both responders’ subgroups – see Figure 6. These sub-regions were correlated with behavioral measures of reduced anxiety and differentiated responders from nonresponders. In addition, nonanxiolytic deactivations were observed in the left lateral amygdala in all treatment sub-groups. No differences were found between treatment responders.

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Figure 6. Illustration of the left and right amygdala subregions commonly affected with treatment. Common anxiolytic effects observed in SSRI and placebo respond-ers are displayed in red. Blue depicts a common nonanxiolytic pharmacodynamic effect observed in responders and nonresponders of SSRIs. Yellow illustrates com-mon effects unrelated to symptom improvement in all subgroups.

4.3.3 Conclusions These results demonstrate that anxiolytic SSRI and placebo treatments are accompanied by overlapping reductions in the left BM/BLA and right VLA. These subregions might constitute an important anxiolytic neuronal target regardless of treatment modality. On the other hand, the observed decreases in the left lateral amygdala sections might reflect nonspecific effects of in-tervention including nonanxiolytic pharmacodynamics and repeated testing. Hence, study III shows that the human amygdala is functionally heterogene-ous with regard to anxiolysis, as previously demonstrated in animals (Green & Vale, 1992), and that effective SSRIs and placebo have common anxiolyt-ic amygdala targets.

4.4 Study IV 4.4.1 Background & aim Although study III shows that successful SSRI and placebo treatments for SAD have common anxiolytic amygdala targets, it is unknown whether they reach these targets via similar or different regulatory pathways. Animal liter-ature documents that medial PFC regions play an important role in regulat-ing amygdala responsivity (Morgan, Romanski, & LeDoux, 1993). Concord-antly, human connectivity studies have reported abnormalities in the amyg-dala-PFC circuitry in SAD patients (Hahn et al., 2011). Moreover, both SSRI


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and placebo interventions have been shown to target PFC regions (Chen et al., 2008; Krummenacher et al., 2009). Study IV aimed at evaluating the differential and common changes in amygdala-PFC functional connectivity patterns between responders and nonresponders to SSRIs and placebo in patients with SAD.

4.4.2 Results Shared negative couplings between the left BM/BLA with the left dlPFC and the right rACC were observed in both treatment responders groups together with a shared positive coupling between the left BM/BLA and the left poste-rior dorsal anterior cingulate cortex (pdACC) – see Figure 7.

Figure 7. Sagital images of shared anxiolytic connectivity patterns in SSRI and placebo responders. On top, a negative correlation is displayed between the left basomedial/basolateral (BM/BLA) and the dorsolateral prefrontal cortex (dlPFC). In the middle, the left amygdala seed is shown to be negatively coupled with the rostral anterior cingulate (rACC). At the bottom, a positive connectivity between the left BM/BLA and the posterior dorsal anterior cingulate (dACC) is depicted. Plots show correlations between change-scores (post-pre) in regional cerebral blood flow in the corresponding amygdala-prefrontal areas.

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Differential connectivity patterns were also noted between treatment re-sponders. Enhanced negative couplings were reported in placebo responders (> SSRI responders) between the left amygdala seed and both the right vmPFC and the right dlPFC. An enhanced positive coupling was also no-ticed in placebo responders as compared to SSRI responders between the left BM/BLA and the right dmPFC. The clinical significance of the rACC, dmPFC and dlPFC – amygdala couplings was further corroborated by com-parisons of responders vs nonresponders within treatment modalities.

4.4.3 Conclusions Study IV shows that sustained SSRI and placebo responses had overlapping and differential amygdala-PFC couplings. Notably, both beneficial treat-ments induced their clinical effects by targeting amygdala-PFC connections, previously reported to be disturbed in SAD (Goldin et al., 2009, Hahn et al., 2011). The overlapping negative couplings observed in treatment responders suggest the existence of a common clinically relevant emotion regulatory pathway. The overlapping positive coupling might also be the reflection of an important beneficial treatment outcome, as activity in this area i.e., the pdACC, has been related to appraisal and expression of fear and anxiety (Etkin et al., 2011).


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5. General discussion

The four studies presented in this thesis investigate the neurobiology under-lying the placebo effect. In the first paper, a review of the placebo neuroim-aging literature, a common prefrontal regulatory mechanism for placebo responses across studies and conditions was noticed. Overall, the ACC emerged as a plausible candidate to mediate the expectancy-driven beneficial influences in subcortical target regions. This review also illustrates the lack of placebo imaging research with clinical populations for a therapeutically relevant period of time, which restricts our ability to generalize the findings to clinical settings.

It is well documented that patients with affective disorders have high pla-cebo response rates (Fournier et al., 2010). However, as evidenced in study I, there is a lack of placebo neuroimaging studies evaluating this phenomenon. To fill this gap, the three empirical studies presented in this thesis examined the neurobiology underlying sustained placebo responses in a clinical popu-lation with SAD and compared these responses with pharmacologically-induced outcomes.

The main findings emerging from this empirical work can be summarized as follows:

• Placebos produce measurable sustained anxiolytic responses both at the neural and behavioral level.

• The TPH2 G-703T polymorphism (i.e., the GG carriers) seems to mediate placebo-induced anxiolytic responses via its effects on amygdalar activity.

• SSRIs and placebo responders share common neuronal anxiolytic targets involving the left BM/BLA and right VLA.

• The amygdala is functionally heterogeneous with regard to anxi-olysis, as anxiety-unrelated effects were also observed in its left lateral sections.

• SSRIs and placebo responders target a common anxiolytic PFC-amygdala pathway that has been previously reported to be dis-turbed in SAD.

• SSRIs and placebo responders share an anxiolytic negative cou-pling between the BM/BLA-dlPFC/rACC, regions known to be associated with emotion regulation.

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• SSRIs and placebo responders share a positive anxiolytic coupling between the BM/BLA-pdACC, possibly reflecting less rumina-tion.

The subsequent sections present a more elaborated discussion on the report-ed findings aiming at further interpreting and integrating the results of this thesis with the existent literature. Hence, without claiming causal relations, possible interpretations and implications of the empirical work as well as suggestions for future research are made. These discussions are comple-mented by a section enumerating some of the drawbacks of these studies that should be considered in future research.

5.1 Placebos and PFC control Overall, neuroimaging evidence, reviewed in study I, suggests that pharma-cologically inactive substances like placebo produce quantifiable, predicta-ble, and replicable neural changes. Despite different methodologies and con-ditions, observed beneficial responses seem to commonly rely on recurrently observed PFC areas such as dlPFC and rACC. Negative correlations between these PFC regions and behavioral improvment (e.g., Kong et al., 2006; Wa-ger et al., 2004) support a modulatory role for these cortical regions in pla-cebo responses.

The dlPFC, known to be involved in generation, maintenance, and ma-nipulation of cognitive representations (Mansouri, Tanaka, & Buckley, 2009), has been shown to play a crucial role in voluntary cognitive control during emotion regulation tasks (Phillips et al., 2008). Due to the neurofunc-tional similarities between reappraisal (i.e., emotion regulatory process) and placebo-induced therapeutic responses, it has been suggested that placebos act by changing the way stimuli are appraised (Oschner & Gross, 2005; Wa-ger et al., 2004). The dlPFC is a plausible candidate to initiate placebo-related regulatory control (Wager et al., 2004). Consistently, a placebo anal-gesia study reported an association between increased anticipatory dlPFC activity and larger placebo response (Wager, Atlas, Leotti, & Rilling, 2011). It is possible that placebo-related expectancies are generated and maintained in the dlPFC that relies on its cortical projections, to induce the downstream placebo positive effects (Atlas & Wager, 2012). Compelling support for the relevance of this region is provided by a study showing that a compromised communication between this cortical region and the rest of the brain disrupts placebo responses in the analgesia domain (Krummenacher et al., 2010). This has been further supported by a pharmacological imaging study show-ing that hyperalgesia induced by naloxone affected the dlPFC. Furthermore, naloxone has also been reported to abolish the analgesic coupling between the rACC and PAG (Eippert 2009a). The rACC, known for its involvement


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in automatic emotion regulation processes (Ray & Zald, 2012) and supported by its privileged prefrontal, limbic, and brainstem connections (Critchley et al., 2003), might constitute the primary route through which phylogenetical-ly newer cortical regions like the dlPFC exert their beneficial influence. Im-aging studies have consistently implicated the ACC in reward expectation and prediction (Mansouri et al., 2009), reinforcing this region as an anatomi-cally important site for placebo modulation. In line with this, negative expec-tancies resulting in a loss of opioid analgesic effect were accompanied by reduced activity in the rostral ACC (Eippert et al., 2009a). These results further support the importance of this cortical region in mediating both pla-cebo and pharmacologically induced therapeutic responses.

According to study I, the ACC is the most frequently reported cortical re-gion in placebo neuroimaging research. Because the direction of the changes observed within this region might appear conflicting, all activations and deactivations were retrieved and interpreted in line with the proposed func-tional dichotomy between ACC subregions i.e., cognitive-dorsal and affec-tive-rostral (Bush et al., 2000). Higher dACC activity observed across condi-tions support the cognitive nature behind placebo-induced expectancies. The rACC was found to be active in placebo anxiolysis but also during placebo acupuncture analgesia. This is consistent with findings implicating this area in regulation of emotional processing (Etkin & Wager, 2007) and also with the pain literature showing involvement of the rACC in descending inhibi-tion of pain (Peyron et al., 2000). Decreases in the cingulate were consistent-ly located in the cognitive-dorsal ACC section. These dACC reductions, however, corresponded to placebo analgesia studies, which is in line with research showing that attenuations in this area might simply represent a re-duction in the affective dimension of pain (Peyron, Laurent, & Garcia-Larrea, 2000). Hence, these attenuations, indirectly suggest an important role for the dACC in affective processing which is not accounted for by this model (Bush et al., 2000).

Recently, however, Etkin and colleagues (2011) proposed a novel dichot-omous functional specialization for the rostral and dorsal ACC to further comprehend the role of ACC in emotional processing. This work was moti-vated by emotional processing findings exhibiting a reliable recruitment of the dACC, which was not considered by the Bush et al. (2000) model. Ac-cording to this new framework, the dACC is involved in expres-sion/appraisal, whereas the rACC is involved in regulation. In line with the proposed dichotomy, figure 8 displays the retrieved placebo-induced ACC increases (left) and decreases (right) enclosed by blue ellipses roughly repre-senting the regulatory region, and red ellipses roughly situated in the ap-praisal/expression region.

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Figure 8. Sagital portrayal of anterior cingulate increases (left) and decreases (right) reported in hemodynamic studies of placebo effect. Red squares correspond to pla-cebo analgesia; green circles illustrate placebo acupuncture analgesia; yellow dia-monds depict affective placebo. Roughly, inside the blue ellipses are the findings reported in the rostral section, whereas the dorsal section is enclosed by red ellipses.

Notably, there are no reported placebo-induced decreases (left panel) in the rACC; all attenuations are observed in the dACC. In line with this model, dACC reductions could mean that placebos successfully decrease negative affective responses which in this particular case are related to noxious pain perception (i.e., affective dimension of pain). The fact that only increases (right panel) were noticed in the rACC suggests that this division of the ACC is more related to regulatory/modulatory mechanisms. Importantly, dACC has been suggested to be involved not only in emotion expression, but also during appraisal processes. Accordingly, placebo-induced recruitment of dACC might be related to affective processes e.g., reappraisal. This emotion processing model constitutes a comprehensible framework to the interpreta-tion of placebo neuroimaging findings, suggesting that affective processing might play a greater role in placebo responsivity than previously acknowl-edged. In line with this, recent placebo analgesia findings reported that activ-ity in affective appraisal circuits predicts placebo responsivity (Wager et al., 2011) and anticipatory anxiety seems to be associated with reduced placebo responsivity (Lyby, Aslaksen, & Flaten, 2010; Morton, Brown, Watson, El-Deredy, & Jones, 2010). It is important to note, however, that mixed ACC findings might also reflect differences in placebo experimental designs (e.g., Wager et al., 2004).

Overall, neuroimaging results suggest that dlPFC and ACC areas play a crucial role in placebo-induced beneficial responses regardless of the condi-tions. Results from study IV further support the therapeutic modulatory role of these cortical regions in sustained placebo and SSRI anxiolysis – see Sec-tion 5.4. Additionally, neuroimaging findings suggest that the benefits caused by placebos also involve disorder-specific neuronal changes that seem to mimic the anatomical structures and the neurochemical processes targeted by pharmacologically active substances.


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5.2 TPH2 gene as a biomarker for placebo anxiolysis Neuroimaging findings reviewed in study I suggest that placebos produce measurable neuronal and behavioral beneficial responses in affective condi-tions (Mayber et al., 2002; Petrovic et al., 2005). Study II further supports this claim by demonstrating bilateral amygdalar attenuations in sustained placebo-induced anxiety relief in a clinical population with SAD. The thera-peutic relevance of these neuronal changes was strengthened by greater amygdala attenuations in placebo responders than in nonresponders. This is in line with amygdala decreases observed after SSRI pharmacotherapy for anxiety disorders (Furmark et al., 2005; Peres et al., 2007), and depression (Fu et al., 2004). Accordingly, amygdalar attenuation seems to be important for successful anxiety-relief regardless of treatment modality and serotonin might be a key neurotransmitter in anxiolytic placebo effects.

The amygdala, with its dense serotonergic innervations (Bauman & Am-aral, 2005), has been reported to be regulated by serotonergic genes (Canli et al., 2005; Hariri et al., 2002). The levels of neurotransmitters carrying mes-sages throughout the brain might be the reflection of genetic differences which possibly affect the likelihood of responding to a treatment. In fact, serotonergic genes have been linked to anxiolytic responses in SAD (Stein et al., 2006). Motivated by the theoretical and clinical implications underlying inter-individual placebo variability, together with the previous knowledge linking serotonergic genes to anxiolytic treatment responsivity, study II combined imaging genetics to investigate the biological markers for in-creased proneness to respond to placebo.

Study II demonstrated that only subjects homozygous for the long allele of the 5HTTLPR or the G variant of the TPH2 G703Tpolymorphism showed attenuated amygdala activity. These findings are in accordance with previous results suggesting that carriers of the s and T alleles have exaggerated amyg-dala responsivity as compared to ll and GG homozygotes (Brown et al., 2005; Hariri et al., 2002). Moreover, the TPH2 polymorphism (i.e., GG car-riers) emerged as a significant predictor of anxiety relief, and this genetic effect on behavioral anxiolysis was mediated by its effects on amygdala activity. Notably, path analysis suggested a pathway from genotype to the behavioral phenotype via the brain endophenotype.

Although study II provided exciting evidence for an anxiolytic modulato-ry role of the TPH2 polymorphism in neuronal and behavioral placebo re-sponsivity, the generalizability of these findings is restricted by the use of a SAD patient sample. However, the importance of affective processing, and the influence of anxiety has, as reported above, started to be unveiled in oth-er placebo domains. Studies of placebo analgesia have reported inversed relations between anxiety levels and pain-relief (Lyby et al., 2010; Morton et al., 2009), as well as anticipatory amygdala decreases (Wager et al., 2007).

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Moreover, internal affective states (Zubietta, Yau, Scott, & Stohler, 2006), and activity in emotional appraisal circuits (Wager & Roy, 2010) were shown to predict placebo analgesia. Notably, and still in the analgesia field, placebo-induced pain relief has been shown to affect general emotion pro-cessing (Zhang & Luo, 2009) which was accompanied by reduced amygdala responsivity (Zhang, Qin, Guo, & Luo, 2011). Together, these studies imply an anxiety reduction in placebo analgesia. Outside the analgesia field, expec-tations of anxiety relief have been reported to modulate the processing of unpleasant stimuli which was accompanied by amygdala decreases (Petrovic et al., 2005). Further support comes from a recent study, outside the fields of pain and anxiety, showing that baseline state anxiety levels together with plasma noradrenaline predicted approximately 60% of placebo response variability in learned immunosuppression (Ober et al., 2012). Hence, it is possible that affective processing, particularly anxiety reduction, has a medi-ating role in the development of placebo responses regardless of the condi-tions.

Genetic evidence gives fuel to a conceivable influence of the TPH2 gene in other placebo-responsive conditions not only through its previously dis-cussed effects on affective processing (i.e., via amygdala responsiveness and anxiety (Gutknecht et al., 2007; Reuter, Kuepper, & Hennig, 2012), but also through its proposed association with executive control functions (Strobel et al., 2007). Notably, subjects homozygous for the T allele have been shown to display diminished executive control in both cognitive and emotional tasks, pointing to abnormally functional high-order control mechanisms for the risk T allele carriers (Osinsky et al., 2009). Considering that placebos may work via emotion/cognitive regulation (Morton et al., 2010; Ochner & Gross, 2005) it is possible that this risk allele is associated with a general impairment in placebo-related regulatory control. As reviewed above (Sec-tion 5.1), the PFC, with its recognized functions in emotion regulation and other forms of cognitive control plays a crucial role in placebo responsivity regardless of conditions. The TPH2 polymorphism has also been shown to affect PFC functions (Baehne et al., 2009). Hence, this might be a relevant biomarker to discern placebo responders from nonresponders not only in SAD but also in other conditions. Based on previous findings it is conceiva-ble that this functional polymorphism mediates general placebo responsivity not only through the amygdala but also by way of the PFC.

It should be also emphasized, however, that placebo responses have di-verse underlying biological underpinnings with many genes probably work-ing in consonance. In line with the reward model, a genetic contribution of the monoamine oxidase-A (MAO-A) and catechol-O-methyltransferase (COMT) genes has also been linked to placebo-induced antidepressant ef-fects (Leuchter, McCraen, Hunter, Cook, & Alpert, 2009). Hence, beyond the serotonergic effect reported in study II, a norepinephrinergic and dopa-minergic effects have been proposed.


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5.3 Anxiolytic subregional amygdala targets Study II revealed an attenuation of amygdala activity in sustained placebo-induced anxiolysis. This is in line with neuroimaging findings from study I proposing that placebos and active pharmacological treatments result in similar beneficial changes. While examining neurofunctional commonalities and differences between pharmacologically and placebo-induced anxiety relief, study III further supports the previous claim by showing that success-ful SSRIs and placebo treatments induce their beneficial effects via common subregional amygdala deactivations. Notably, these attenuations were corre-lated with behavioral measures of anxiety relief and differentiated respond-ers from nonresponders in both treatment groups.

As previously discussed, alleviation of SAD is associated with attenuated amygdala responsivity not only after successful SSRI and placebo treatments (Furmark, 2002), but also after cognitive behavioral therapy (CBT) treat-ments, supporting the crucial role of this region in anxiolysis. However, counterintuitive amygdala changes have also been reported (e.g., Britton et al., 2005; Maslowsky et al., 2010). Likewise, in study III, attenuated subre-gional amygdala responsivity was also noticed in treatment nonresponders, and these changes, located in the left lateral amygdala, were unrelated to anxiety relief. Notably, findings from study III indicate that only certain sections of the amygdala (i.e., left BM/BLA and right VLA) mediated anxie-ty relief, whereas other amygdalar sections (i.e., left lateral amygdala), were reduced in all treatment subgroups, reflecting treatment nonspecific effects not tied to symptom improvment.

Hence, these results suggest that the amygdala is a functionally heteroge-neous region with regard to anxiolysis. Accordingly, animal studies have long shown that the amygdala is neither an anatomical nor a functional ho-mogeneous region (Swanson & Petrovich, 1998). Findings coming from human neuroimaging studies, investigating the functional neuroanatomy of the human amygdala, are largely consistent with animal models (Roy et al., 2010, Etkin, Prater, Schatzberg, Menon, & Greicius, 2009). Notably, the BLA has been shown, in both animal and human research, to have a privi-leged communication with the medial prefrontal cortex, and this crosstalk has been suggested to play an important role in emotion regulation (Etkin et al., 2009, Kim et al., 2011). Input coming from these cortical regions is thought to inhibit the amygdala output, by regulating the BLA inputs to the central amygdala (Kim et al., 2011). Considering the crucial role played by

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these cortical regions (noted in study I) in beneficial placebo responses, this is a likely inhibitory circuit for placebo anxiolysis.

The anxiolytic response of placebo was indistinguishable from the re-sponse resulting from SSRIs. Hence, it is conceivable that the common amygdalar attenuations reflect expectancy-induced anxiolysis. The founda-tion of placebo-controlled randomized trials lies within the assumption that any pharmacological treatment comprises both physiological and psycholog-ical components. However, especially in the affective disorder domain, the magnitude of the psychological component creates a methodological chal-lenge when trying to establish the efficacy of new pharmacological treat-ments (Alphs et al., 2012). In line with this, concerns have been raised by meta-analytic studies comparing antidepressants such as SSRIs with placebo (Kirsch et al., 2008). According to these studies, for the majority of the pa-tients, the difference between active compounds and placebos is not clinical-ly significant. The relevance of the placebo effect in anxiolysis is also sup-ported by a study showing that anxiolytic responses to diazepam were abol-ished when covertly administered (Colloca et al., 2004). The anxiolytic ef-fect was noticed solely after the open administration of diazepam, suggesting a placebo effect (Colloca et al., 2004). These findings are not restricted to the anxiety domain; similar clinically relevant effects have been reported both at a behavioral and neurofunctional level in pain and PD (Bingel et al., 2011; Colloca et al., 2004).

Even though it is conceivable that anxiolytic pharmacological treatments work partly via psychological components, study III does not provide suffi-cient evidence to support this claim. Compelling evidence, arising from mi-croinjection and c-Fos studies in the animal literature, supports the BLA as a likely target for SSRI anxiolytic effects (Inoue et al., 2004; Izumi, Inoue, Kitaichi, Nakagawa, & Koyama, 2006). Therefore, the observed anxiolytic attenuations might represent therapeutic targets under the influence of both successful pharmacodynamic and psychological therapeutic effects.

Additional analyses were performed in order to identify the underlying genetic modulators of successful SSRI interventions. Results indicated a link between the regulatory region of the serotonin transporter gene, 5HTTLPR, and SSRI responsivity. In line with previous pharmacological studies (e.g., Stein et al., 2006), ll homozygosity was shown to be associated with a better SSRI outcome, while the TPH2 polymorphism was not related to SSRI out-come. These results are somehow contrasting the predictive role attributed to the TPH2 in placebo responsivity (Study II). Accordingly, even though SSRIs and placebo target common neural mechanism, we could not demon-strate a common gene-brain-behavior pathway.


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5.4 Amygdala-prefrontal anxiolytic couplings In study III, left BLA/BM and right VLA attenuations were associated with successful treatment responses. Although these findings suggest common subregional amygdala targets for both effective treatment modalities, no answers were provided in study III as to whether these treatments reached their anxiolytic targets via similar or differential neural paths. As previously discussed, the PFC is known to play an important role in regulating amygda-la responsivity (Ray & Zald, 2012), and PFC-amygdala abnormalities have been demonstrated in SAD patients (Goldin et al., 2009; Hahn et al., 2011). In study IV, while investigating overlapping amygdala-PFC connectivity patterns associated with clinical response, a shared negative coupling be-tween the left amygdala seed, and the left dlPFC as well as the right rACC was noticed in responders. Due to the more liberal statistical threshold used in these analyses, these results should be interpreted with caution.

Thought to prompt the downstream circuits involved in beneficial placebo responses (Eippert et al., 2009a; Krummenacher et al., 2010; Wager et al., 2004; Zubietta et al., 2005), the dlPFC, has also been implicated in success-ful SSRI treatment (Chen et al., 2008; Fales et al., 2009). The rACC, which emerged in study I as the most commonly reported region in placebo respon-sivity, has also been shown to be a part of a shared mechanism for opioids and placebo analgesia treatments (Petrovic et al., 2002). The relevance of this region in pharmacological treatments is also supported by imaging stud-ies in the affective disorder domain (Mayberg et al.,1997; Whalen et al., 2008).

In line with our findings, a normalized functional PFC-amygdala connec-tivity, involving a negative coupling between dlPFC and rACC with the left amygdala, has been reported in depressed patients after SSRI treatment (Chen et al., 2008). This is consistent with the view that successful anxiolyt-ic treatments enhance the cortical regulation of abnormal limbic activation (Anand et al., 2005, Mayberg, 2002). In study IV, the observed common recruitment of these PFC areas indicates that both effective treatments work by normalizing an anxious cognitive processing mode that relies on altered PFC-amygdala connectivity, tentatively suggesting a common mechanism of action.

It seems that SSRI-intake affects cognitive processing at an early stage, reducing cognitive biases like exaggerated threat-related interpretations in anxiety patients (Mogg, Baldwin, Brodrick, & Bradley, 2004) and attentional vigilance towards threat in healthy volunteers (Murphy et al., 2009). Short term SSRI administration has also been reported to reduce negative self-referential processing in the PFC in subjects at risk for depression. This sup-ports the notion that pharmacological treatments induce their beneficial ef-fects, at least partly, by regulating dysfunctions associated with negative cognitive biases (Di Simplicio et al., 2012). Furthermore, resting state con-

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nectivity findings suggest that SSRIs reshape cortical control after 7 days of administration, making the PFC a plausible target for SSRI drug action (McCabe & Mishor, 2011). Notably, remediation of cognitive biases is also an important goal for psychological treatments of anxiety. Placebos have been reported to induce cognitive changes (Morton et al., 2010) like reduced unpleasantness of negative stimuli (Petrovic et al., 2005).

Hypothetically, if PFC-dependent expectancies of improvement are a therapeutically important common denominator for all treatment responders (Hunter, Leuchter, Morgan, & Cook, 2006), it is possible that successful SSRIs and placebo, share a common cognitive and neural mechanism of action. By maintaining a representation of expectancies, the dlPFC might exert top-down control of the amygdala via the rACC in both treatment mo-dalities. However, due to the correlative nature of study IV and without measuring expectancies, it is not possible to draw any conclusions with re-gard to the driving forces behind these beneficial responses.

In fact, it is equally likely that in SSRI treatment, involvement of these PFC regions is not a reflection of a top-down process but rather a conse-quence of amygdala attenuation e.g., BLA (Izumi et al., 2006), that remedies the abnormal recruitment of PFC (Fales et al., 2009). With the intention to clarify the brain targets of SSRI treatments, microinjection of SSRIs into the BLA, mPFC, and thalamus was examined during conditioned fear. Notably, the beneficial effects resultant of SSRI administration were specific to the amygdala (Inoue et al., 2004), supporting this region as a likely target of SSRIs.

Additionally, in study IV the pdACC was found to be positively connect-ed to the left BM/BLA in both successful treatments. The observed pdACC reduction, which was accompanied by reduced amygdala activity, is in line with the proposed role of this area in expression of anxiety (Etkin et al., 2011). Hence, pdACC changes might simply reflect a reduction in expres-sion of anxiety. The rostral vs dorsal ACC/mPFC contrast found in study IV supports the proposed dichotomous role of ACC/mPFC in the emotional processing model mentioned in section 5.1. According to our results, emo-tion regulation does not rely on a simplistic top-down regulatory model. Instead, emotional experience seems to be determined by two separate net-works. The rostral section of the ACC/mPFC which seems to play a part in regulation, contrasts with the dorsal ACC/mPFC portion involved in ap-praisal and expression of emotion.

It is noteworthy that differential connectivity patterns, between treatment responders, were also observed in study IV. In fact, the right dmPFC was shown to be more positively correlated with the left amygdala in placebo responders than in SSRI responders. Differential negative couplings were also observed between the left amygdala seed with the right vmPFC and the right dlPFC. These results suggest that placebo responders might have a stronger regulatory path than SSRI responders. Considering anticipation of


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relief as the driving force behind these neurofunctional changes, these results might come as a surprise if one expects SSRIs to act as active placebos i.e., by boosting the expectancy effect. On the other hand, it is also conceivable that the side effects of SSRIs affect the internal affective state of the patients (e.g., Zubieta, Yau, Scott, & Stohler, 2006) disturbing the placebo response. However, due to the fact that the differential neural changes were unrelated to a differential clinical outcome, and since expectancies were not assessed, the therapeutic significance of these findings is uncertain.

Research in major depression has, however, also suggested that placebo treatment induces changes in brain function distinct from those observed with antidepressive medication (i.e., SSRIs and SNRIs) (Leuchter et al., 2002). In fact, placebo responders have been shown to induce a unique in-crease in prefrontal EEG, advising that placebo response is not physiologi-cally equivalent to pharmacological response. This claim has recently re-ceived support from the analgesia field (Kong et al., 2009, Atlas et al., 2012). For instance, placebo analgesia has been shown to engage more PFC activity (i.e., lOFC and vlPFC) as compared to opioid analgesia (Petrovic et al., 2010). These findings were reported to be related to stronger expecta-tions induced by placebo and larger error signals i.e., stronger mismatch between external and internal signals. During opioid intake, nociception is thought to result from direct opioidergic receptor activation which in turn might reduce expectations and error signals. During placebo analgesia, how-ever, since expectations do not match nociception, it is possible that there is a larger sustained expectation that is incongruent with the processed noci-ceptive input, thus increasing the error signal (Petrovic et al., 2010). This is in line with a Bayesian processing perspective proposing that perceptions of the outer world are the result of a negotiation between incoming external inputs and the internal expectations of the system (Friston, 2005). It is, how-ever, unlikely that the couplings differentiating placebo from SSRI response in study IV are related to differential higher expectancies attributed to pre-dicting error signals in the placebo group. Unlike opioids, SSRIs are thought to have a considerable therapeutic delay between drug intake and perceived benefits. Hence, placebo-induced expectation might not differ from SSRI-induced expectation, at least not as much as with opioid-induced expecta-tion. Relying on the additive principle that RCTs are based on, the shared amygdala-PFC couplings reported in study IV might represent a common anxiolytic pathway, driven by expectancies of improvement, in both SSRIs and placebo treatments.

In study IV, treatment-related amygdala-PFC connectivity changes were more salient for the left amygdala than for the right. Also in depressed pa-tients, altered functional connectivity of the amygdala has previously been shown to be more salient for the left amygdala after SSRI treatment (Chen et al., 2008). Moreover, disrupted connectivity between the left amygdala and the PFC has also been reported in patients with SAD (Hahn et al., 2011),

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suggesting that effective treatments might work by normalizing the com-promised left amygdala connectivity patterns.

5.5 Limitations There are several limitations regarding the empirical work presented in this thesis that are worth considering not only when interpreting the findings but also in the design of future studies.

5.5.1 Natural history control group As previously discussed, an observed improvement in the placebo group does not necessarily imply that placebo is causing this improvement. To be able to demonstrate that placebos are responsible for the observable respons-es, the same scientifically principles applied to evaluate active treatments should be used (i.e., a control group). It is important to mention that the dou-ble-blind RCTs were initially designed to study active treatments. The inclu-sion of another control group would have raised ethical concerns. The Decla-ration of Helsinki states that: “In any medical study, every patient - includ-ing those of a control group, if any - should be assured of the best proven diagnostic and therapeutic methods”. This is perhaps one reason as to why placebo analgesia is by far the most investigated placebo response – because it is possible to experimentally manipulate pain in healthy volunteers.

Moreover, even though the RCTs on which this thesis is based were not perfectly designed to investigate the placebo response, it is still possible, with some certainty, to rule out the confounding factors: 1. Spontaneous Remission: Longitudinal studies have shown that SAD is a

chronic condition that remains stable if not properly treated (e.g., Yon-kers et al., 2001). Literature on waiting-list control groups in the treat-ment of SAD suggests that waiting for treatment does not improve or al-ter the condition (e.g., Furmark et al., 2009). Importantly, previous work has evaluated waiting-list controls in the neuroimaging context (Furmark et al., 2002) and there were no signs of remission or altered brain activity in the amygdala or fear network.

2. Regression towards the mean: Placebo responders did not differ from placebo nonresponders in symptom severity at pre-treatment, suggesting that regression to the mean does not explain the clinical response ob-servable in placebo responders. Similarly, there were no initial differ-ences with regard to brain activity that would give rise to such concerns.

3. Response bias: This would become a problem with the inclusion of a no treatment control group, which was not the case. In these studies a dou-ble-blind randomized procedure was used.


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5.5.2 Measuring expectancies Expectations are central when studying the placebo phenomenon. However, in the trials that this thesis is based on, it was not possible to properly evalu-ate the impact of these psychological factors on neurofunctional changes and behavioral outcomes because active placebos were not used and expectan-cies were not measured. Moreover, when comparing pharmacological thera-pies with placebos, pharmaco-induced side effects might result in unwanted unblinding of the trials. Unfortunately, it was also not possible to retrieve the complete safety data in one trial. It was previously reported, however, that there were 43 vs 23 drug-related adverse events in the citalopram and place-bo group respectively, the most common being headache, tiredness, insom-nia, nausea, irritability and somnolence. Events were generally mild or mod-erate and all were resolved (Furmark et al., 2005).

Hypothetically, a difference in the perception of the side effects between SSRI responders and placebo responders could have possibly boosted the SSRI response and reduced the placebo response. Although an active place-bo was not used, a strong placebo effect is still observed. Conversely, it is also possible that SSRIs side effects affected patients’ internal affective state, with a chronic increase in anxiety that might affect the placebo re-sponse.

Regarding the difference between placebo responders and placebo nonre-sponders’ one would like to think that low expectancies underlie the high number of placebo nonresponders frequently observed in RCTs, since most trials are not specifically designed to boost the placebo response. According-ly, it is interesting that meta-analysis of available antidepressant trials using active placebos suggested that the difference between antidepressants and active placebo was much smaller than in trials using inert placebos (Moncrieff, 2002).

Clinical trials assume that placebo effects are independent of pharmaco-logical effects and thus can be subtracted from the pharmalogical response to achieve an accurate estimation of the pharmacological effect. This subtracted effect constitutes the shared factor observed in both pharmacological and placebo treatment allegedly related to expectancies of symptom improve-ment. Hence, the common amygdala deactivations observed in study III and the common amygdala-PFC couplings reported in study IV might indeed represent an expectancy-induced response.

5.5.3 Chronic vs. acute treatment effects Due to the fact that the last dose of both SSRIs and placebo were adminis-tered 2-4 hours before the final PET assessment, it might be argued that the measured neuronal effects reflected an acute response rather than a sustained response. Nevertheless, although our design does not allow for a clear differ-entiation between the acute and chronic treatment effects on neural activity,

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the behavioral measures support a sustained anxiolytic effect for both treat-ments.

5.5.4 Merging different SSRIs with different doses In study III and IV different SSRI arms were merged. In Figure 3 - Section 3.2, it is possible to observe a tendency towards a higher number of respond-ers in the 20 mg paroxetine group (the highest dose) compared with the 7.5 mg group (the lowest dose) while the outcome in this group was comparable to Citalopram 40 mg. When statistically comparing the outcome of the two paroxetine groups on the LSAS, no significant differences were found. Analysis presented in the supplem entary material of study III also supports the conclusion that paroxetine 7.5 mg is neurofunctionally different from placebo and does not deviate signifi-cantly from the paroxetine 20 mg arm in terms of amygdala attenuation. Hence, these results indicate that 7.5 mg paroxetine could be merged with the other SSRI arms. A more thorough examination of this question would ideally involve comparisons of the different paroxetine groups with placebo on serotonin transporter availability with an appropriate PET ligand. Unfor-tunately this was not possible.

5.5.5 PET spatial and temporal resolution Although PET has a limited spatial resolution, when extracting the voxels resulting from the conjunction analysis in study III, the voxels with the local maxima in left BM/BLA and right VLA were positively correlated with clin-ical anxiety measures, whereas the ones peaking in the left lateral amygdala were not. So, the functional differences observed between these regions strengthen the conclusion regarding amygdala heterogeneity in study III. Moreover, the results were stable when using different filters. Also, in paper III the emphasis was put on amygdala sections and subregions, rather than subnuclei. Labeling the areas based on location of the peak voxel was, how-ever, the most pedagogical way to describe the results. Notably, the exten-sion of clusters goes beyond the subregion in which the maximum voxel was found. Nevertheless, the smallest interpeak distance between the reported anxiolytic and nonanxiolytic findings was above 8mm suggesting that the clusters did not overlap. Furthermore, due to its limited spatial and temporal resolution, PET it is not the optimal technique to assess functional connec-tivity reported in study IV. Nonetheless, the use of techniques with better spatial and temporal resolution such as fMRI would limit the implementation of the ecologically valid anxiogenic public speaking task.

5.5.6 Imaging baseline The design implemented in the three trials lacked a proper imaging baseline i.e., imaging control condition like resting-state or private speaking. There-fore, since it was not possible to evaluate pretreatment neuronal reactivity, it


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could be argued that neurofunctional changes observed after treatment re-flects lower trait-like activity. However, with regard to the amygdala, previ-ous research suggests that attenuated amygdala activity is related to state anxiety reduction (Furmark et al., 2005).

5.5.7 Connectivity analysis Connectivity analysis can be divided into two distinct subtypes of analyses. Functional connectivity, where correlative methods are applied to assess the associations between two anatomically distinct brain regions, and effective connectivity that measures the direction of the influence (Friston 1994, Fris-ton et al., 1997). In study IV a simple regression analysis was performed to measure the association between the amygdala and the PFC. This procedure evaluates the functional networks working in a co-operative fashion. Never-theless, due to the correlative nature of these analyses, no causal inferences are allowed regarding driving vs. receiving regions. For example, increased negative PFC-amygdala connectivity with treatment could reflect a top-down process where increased cortical activity restore limbic dysfunction as well as a bottom-up process where reduced limbic activity restore prefrontal dysfunction. For cause-effect inferences, effective connectivity analyses employing methods like dynamic causal modeling or Granger causality analysis are needed. Moreover, the present study focused on amygdala-prefrontal connectivity only, excluding other relevant regions such as the insula, ventral striatum and brainstem which may also be involved in anxio-lytic treatments.

5.5.8 Power Due to small sample sizes, when comparing subgroups of responders and nonresponders, an increased risk of type-II error might be a considerable factor affecting the results. Nevertheless, supported by a theoretical back-ground a ROI approach has been used, which decreases the number of com-parisons, supposedly giving more power to the analyses. In some cases, a more liberal statistical threshold, commonly accepted in neuroimaging stud-ies (Lieberman & Cunningham, 2009), has also been used. Nevertheless, and even though these results are promising, they need to be replicated and inter-preted with caution.

5.6 Concluding remarks The empirical work in this thesis constitutes a first step towards understand-ing the neurobiology of a sustained placebo response in the treatment of anxiety. The use of a randomized double-blind design, with prolonged rather than acute administration of placebo in a clinical population, together with

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the use of a stressful public speaking task, increased the ecological validity. The results emerging from this thesis provide evidence of a sustained place-bo-induced anxiety reduction both at the neural and behavioral level. Nota-bly, the TPH2 G703T polymorphism emerged as a significant predictor of clinical placebo response, and this response was mediated by its effect on amygdala attenuation. Moreover, similar to effective SSRIs, effective place-bo treatment seem to target specific anxiety-related amygdala subregions (BM/BLA, VLA), whereas neural changes in other amygdala subregions appear to reflect nonspecific effects of intervention. Beneficial placebo and SSRI treatments, also, seem to involve both common and differential amyg-dala-PFC couplings. Symptom improvement, in both treatment modalities, seems to be modulated by two distinct networks seemingly engaged in con-trolling and expressing anxiety.

These findings provide some clues about the biological factors that ac-count for individual variation in placebo responsivity. Although individual differences are probably determined by complex interactions, imaging genet-ic studies open new opportunities to explore how genetic variation influ-ences brain activation patterns and the associated phenotypes. The TPH2 G703T polymorphism might be associated with susceptibility to respond to placebo also in other placebo-responsive conditions. Because expecting a positive clinical outcome is accompanied by anxiety reduction regardless the conditions (Price, Finniss, & Benedetti, 2008), understanding the neurobiol-ogy of placebo-induced anxiolysis could be relevant for the placebo field at large. Future research should address the influence of the TPH2 and other gene variants on affective processing networks across placebo responsive conditions.

The findings of this thesis also point to the importance of investigating the underlying neurobiological mechanisms behind successful SSRI treat-ment. A therapeutically relevant topic requiring further investigation is to what extend SSRIs induce clinical effects that go beyond active placebos. It is still a matter of speculation and debate, how much of the clinical effect induced by SSRIs is explained by cognitive expectancies. With regard to the controversy surrounding the efficacy of SSRIs, the use of public speaking task and neuroimaging of SAD patients might constitute a valid clinical model to investigate the beneficial therapeutic effects of placebo relative to SSRIs. The ultimate goal of this research is to facilitate the translation of scientific knowledge into improved patient care.

Concerning the proneness to become a placebo responder, stable individ-ual differences, such as the ones identified in study II, might have important implications in both clinical research and clinical practice. Having prior knowledge about relevant outcome predictors enables an adjustment of treatments to match the specific needs of the individual patients e.g., person-alized therapy. Moreover, this prior information might be equally valuable


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during patient selection in RCTs allowing for a control of individual varia-bility.

Although better understanding of the neural mechanisms behind the pla-cebo response may have profound importance for clinical research and prac-tice, research on this topic is still in its infancy. Many clinicians prescribe placebos in their clinical practice and consider this practice to be ethically justifiable (Tilburt, Emanuel, Kaptchuk, Curlin, & Miller, 2009). Ethical issues regarding deception in the field of placebo are an important concern and recently, it has been shown that deception is not actually needed to achieve a therapeutic response (Kaptchuk et al., 2011). Moreover, the open-hidden administration of drugs might also constitute a good alternative to overcome the ethical constrains with the use of placebos in clinical trials. Nevertheless, without proper scientific knowledge regarding the underlying therapeutic mechanisms and the clinical conditions affected, the usage of placebos in clinical practice has a long way to go.

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6. Acknowledgments

“…the individual is defined only by his relationship to the world and to oth-er individuals; he exists only by transcending himself, and his freedom can be achieved only through the freedom of others. He justifies his existence by a movement which, like freedom, springs from his heart but which leads outside of himself.”

Simone de Beauvoir

It is truly hard to describe how amazing this PhD journey has been and the impact that a number of extraordinary people have had in my work and in my life during the past years - I have been given so much…

First and foremost I owe my deepest gratitude to two brilliant minds that I had the pleasure to have as mentors: Tomas Furmark and Mats Fredrikson - I am deeply grateful for your scientific guidance, generosity, warmth, support, and enthusiasm. Tomas, you have been fantastic. You have provided the guidance, the support, and the right amount of critical input to make me evolve as a researcher. That was probably not easy as I can be quite stubborn (and that’s why I had to overdo it ☺). I think of you as a genuinely kind person and I am extremely grateful to have you not only as a mentor but also as a friend. Mats, your passion for science is truly inspirational - it is one of the reasons why I moved to Sweden. I remember the first time I heard you speaking about your research in Lisbon, right then I realized what I wanted to do. Today, your charisma and your expertise continue to be a source of inspiration. I consider you not only an amazing scientist but also an amazing person (with a Happy dog ☺). It has been a true honor to be a part of your team!

I would also like to express my sincere gratitude to past and present members of this wonderful team: Fredrik Åhs, Clas Linnman, Åsa Michelgård, Thomas Ågren, Jonas Engman, Malin Gingnell, Andreas Frick (Kusin), Ulrika Wallenquist, Johannes Björkstrand, Iman Alaie, and Nathalie Peira - it has been a privilege to work with such an amazingly talented group of people that I have the pleasure to call friends. You make me want to be a better researcher and a better person every day! I also wish to thank all the co-authors for the shared knowledge and good collaboration. A special thanks goes to Fredrik Åhs for his generosity and invaluable help, and to Lieuwe Appel for all the hard work and for his valu-


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able comments. Another special thanks goes to Ata Ghaderi, and Hedvig Söderlund for providing great feedback on my thesis.

A heartfelt thanks goes to the amazing group of people that makes the Psychology department in Uppsala such an wonderful place to work - Your smiles and small talks made my world a happier place. In particular, I have to mention some beautiful individuals that have played a major role in this journey: Jessika Karlsson, Hällena Grönqvist, Malin Brodin, Simon (Lilly), Hanna Skagerström, Thomas Ågren, and Åsa Michelgård - Thank you for your valuable friendship. You have made me feel welcome from day one. Karin Brocki, Ulrika linda, Robin Bergh (my favorite time-waster), Clarinha Asún, Claudi Esner, Jonas Engman, Kusin, and Tommie Forslund - You have made my days brighter and warmer. You have a special place in my heart.

I also wish to thank some dear friends outside the department for making this journey extra special: Elin Bannbers, Jürg Brendan Logue, Brian Huser, Erica Comasco, Tiago Braga, Daniela Patinha, Ana Nunes, Sara Massena, Luísa Batalha, and Ana Margarida. In one way or the other, you have been there for me and I am grateful for that.

Gladys Garcia, Judith Garcia, y macaquinhos - You have had a major im-pact in my life. I feel truly blessed to have you around - los quiero mucho.

Together, all of you beautiful people have made Uppsala home! But if home is where your heart is, then I have to say that mine is scattered a bit everywhere… Ana Sofia, and Claúdia Ribeiro - my favorite girls, I miss you every day. Rui Rodrigues, even though this journey has changed the course of our lives you are one of the most amazing persons that I have ever met. To my beautiful family: Mãe Mariana, Pai Zézinho, Irmano Renato e Cristi-na linda, I want to express my deepest appreciation for the invaluable love and everlasting support that I get from you. You have always believed in me and supported me through all my decisions even when that meant that you would be physically apart from your pintaínha - I love you!

Vanda Faria, Uppsala, 26th of September 2012

For finantial support I am in debt with GlaxoSmithKline, the Swedish Re-search Council, the Swedish Council for Working Life and Social Research, Non-graduated Researchers Fund from Uppsala University, and the Founda-tion of Science and Technology from the Portuguese Ministry of Science, Technology and Higher Education co-financed by the European Social fund-ing.

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